mirror of
https://github.com/firestar5683/StarPilot.git
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firestar5683
3042 lines
139 KiB
Python
Executable File
3042 lines
139 KiB
Python
Executable File
#!/usr/bin/env python3
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import math
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import numpy as np
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import time
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import cereal.messaging as messaging
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from opendbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
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from openpilot.common.constants import CV
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.common.realtime import DT_MDL
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.starpilot.common.model_versions import is_tinygrad_model_version
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from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import desired_follow_distance
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import should_trigger_planner_fcw
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import STOP_DISTANCE
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC
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from openpilot.selfdrive.controls.lib.lead_behavior import is_radarless_matched_follow_window
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
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from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
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from openpilot.common.swaglog import cloudlog
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LON_MPC_STEP = 0.2 # first step is 0.2s
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A_CRUISE_MIN = -1.0
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A_CRUISE_MAX_BP = [0.0, 5., 10., 15., 20., 25., 40.]
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A_CRUISE_MAX_VALS = [1.125, 1.125, 1.125, 1.125, 1.25, 1.25, 1.5]
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CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
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ALLOW_THROTTLE_THRESHOLD = 0.4
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ALLOW_THROTTLE_HYSTERESIS = 0.05
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ALLOW_THROTTLE_ENABLE_THRESHOLD = ALLOW_THROTTLE_THRESHOLD + ALLOW_THROTTLE_HYSTERESIS
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ALLOW_THROTTLE_DISABLE_THRESHOLD = ALLOW_THROTTLE_THRESHOLD - ALLOW_THROTTLE_HYSTERESIS
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MIN_ALLOW_THROTTLE_SPEED = 5.0
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RAW_LEAD_SAFETY_MIN_CLOSING_SPEED = 0.5
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RAW_LEAD_SAFETY_TTC = 7.0
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RAW_LEAD_SAFETY_DISTANCE = 40.0
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RAW_LEAD_LOW_SPEED_HOLD_MAX_EGO_SPEED = 4.5
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RAW_LEAD_LOW_SPEED_HOLD_MAX_LEAD_SPEED = 3.5
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RAW_LEAD_LOW_SPEED_HOLD_MAX_DISTANCE = 10.0
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RAW_LEAD_LOW_SPEED_HOLD_MAX_LATERAL_OFFSET = 1.75
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RAW_LEAD_LOW_SPEED_HOLD_MIN_CLOSING_SPEED = 0.15
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STANDSTILL_LEAD_NUDGE_ACCEL = 0.05
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STANDSTILL_LEAD_NUDGE_MIN_SPEED = 0.0
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STANDSTILL_LEAD_NUDGE_MIN_LEAD_ACCEL = 0.2
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STANDSTILL_LEAD_DEPART_MIN_ACCEL = 0.35
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STANDSTILL_LEAD_DEPART_MAX_EGO_SPEED = 1.5
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STANDSTILL_LEAD_DEPART_MIN_LEAD_SPEED = 0.6
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STANDSTILL_LEAD_DEPART_MIN_GAP_MARGIN = 0.8
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STANDSTILL_LEAD_DEPART_MIN_MODEL_ACCEL = 0.08
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LEAD_DEPART_CONFIDENT_MIN_GAP = 3.75
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LEAD_DEPART_CONFIDENT_MAX_GAP = 5.25
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LEAD_DEPART_CONFIDENT_MIN_LEAD_SPEED = 0.3
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LEAD_DEPART_CONFIDENT_MIN_LEAD_DELTA = 0.25
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LEAD_DEPART_CONFIDENT_MIN_LEAD_ACCEL = 0.2
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LEAD_DEPART_CONFIDENT_CONFIRM_TIME = 0.35
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STANDSTILL_STOPPED_LEAD_GUARD_MAX_EGO_SPEED = 0.5
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STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_SPEED = 0.45
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STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_DELTA = 0.35
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STANDSTILL_STOPPED_LEAD_GUARD_MIN_MODEL_PROB = 0.95
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STANDSTILL_STOPPED_LEAD_GUARD_MAX_LATERAL_OFFSET = 1.75
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STANDSTILL_STOPPED_LEAD_GUARD_MIN_DISTANCE = 3.0
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STANDSTILL_STOPPED_LEAD_GUARD_DISTANCE_MARGIN = 3.0
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STANDSTILL_STOPPED_LEAD_GUARD_MIN_BRAKE = 0.16
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STANDSTILL_STOPPED_LEAD_GUARD_MAX_BRAKE = 0.26
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RADAR_DEPART_CONFLICT_MAX_EGO_SPEED = 1.6
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RADAR_DEPART_CONFLICT_MIN_RADAR_LATERAL = 1.5
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RADAR_DEPART_CONFLICT_MAX_RADAR_DISTANCE = 18.0
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RADAR_DEPART_CONFLICT_MIN_MODEL_PROB = 0.95
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RADAR_DEPART_CONFLICT_MAX_MODEL_DISTANCE = 18.0
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RADAR_DEPART_CONFLICT_MAX_MODEL_LATERAL = 0.9
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RADAR_DEPART_CONFLICT_MAX_MODEL_LEAD_SPEED = 2.0
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RADAR_DEPART_CONFLICT_MAX_DISTANCE_MISMATCH = 4.0
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LEAD_DEPART_ACCEL_HOLD_TIME = 1.2
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LEAD_DEPART_ACCEL_HOLD_MAX_EGO_SPEED = 1.5
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LEAD_DEPART_ACCEL_HOLD_MIN_LEAD_SPEED = 0.6
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LEAD_DEPART_ACCEL_HOLD_MIN_LEAD_DELTA = 0.5
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LEAD_DEPART_ACCEL_HOLD_MIN_GAP = 3.5
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LEAD_DEPART_ACCEL_HOLD_FULL_GAP = 6.0
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LEAD_DEPART_ACCEL_HOLD_FULL_LEAD_SPEED = 2.2
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LEAD_DEPART_ACCEL_HOLD_MIN_MODEL_PROB = 0.85
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LEAD_DEPART_ACCEL_HOLD_MIN_MODEL_ACCEL = 0.12
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LEAD_DEPART_ACCEL_HOLD_MAX_LEAD_BRAKE = 0.2
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LEAD_DEPART_ACCEL_HOLD_MIN_ACCEL = 0.25
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LEAD_DEPART_ACCEL_HOLD_MAX_ACCEL = 0.45
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_EGO_SPEED = 4.5
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_DISTANCE = 4.0
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_DISTANCE = 18.0
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_SPEED = 4.0
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET = 1.75
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_MODEL_PROB = 0.9
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_LEAD_DELTA = -0.5
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_DELTA = 0.75
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_LEAD_ACCEL = -0.4
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_ACCEL = 0.25
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_ACCEL = 0.08
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_ACCEL = 0.22
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MAX_EGO_SPEED = 1.25
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_GAP = 4.0
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_LEAD_SPEED = 1.2
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_LEAD_DELTA = 0.8
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LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_LEAD_ACCEL = 0.5
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CLOSE_LEAD_BRAKE_CAP_MAX_TTC = 25.0
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VISION_LEAD_APPROACH_MIN_CLOSING_SPEED = 2.0
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VISION_LEAD_APPROACH_TRIGGER_TIME = 4.5
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VISION_LEAD_APPROACH_FULL_TIME = 1.0
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VISION_LEAD_APPROACH_TIGHT_BUFFER = 2.0
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VISION_LEAD_APPROACH_MAX_DECEL = 0.80
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VISION_LEAD_APPROACH_MIN_DECEL = 0.15
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VISION_LEAD_APPROACH_MIN_MODEL_PROB = 0.85
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VISION_LEAD_APPROACH_FULL_MODEL_PROB = 0.98
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VISION_LEAD_APPROACH_DEFICIT_MAX_DECEL = 1.30
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VISION_LEAD_APPROACH_DEFICIT_BUFFER_MIN = 3.0
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VISION_LEAD_APPROACH_DEFICIT_BUFFER_GAIN = 0.20
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VISION_LEAD_APPROACH_BRAKING_DEFICIT_MIN = 0.75
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VISION_LEAD_APPROACH_BRAKING_MIN_LEAD_BRAKE = 0.45
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VISION_LEAD_APPROACH_BRAKING_FULL_LEAD_BRAKE = 1.20
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VISION_LEAD_APPROACH_BRAKING_FLOOR_MIN_DECEL = 1.30
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VISION_LEAD_APPROACH_BRAKING_FLOOR_MAX_DECEL = 1.75
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VISION_LEAD_APPROACH_CONFIRM_TIME = 0.25
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VISION_LEAD_APPROACH_CONFIRM_BYPASS_DECEL = 1.0
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VISION_LEAD_APPROACH_CONFIRM_BYPASS_CLOSING_SPEED = 4.0
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VISION_LEAD_APPROACH_CONFIRM_BYPASS_LEAD_BRAKE = 0.20
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VISION_LEAD_APPROACH_CONFIRM_BYPASS_DISTANCE_MIN = 28.0
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VISION_LEAD_APPROACH_CONFIRM_BYPASS_DISTANCE_TIME = 0.85
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VISION_UNTRACKED_SLOW_LEAD_MIN_MODEL_PROB = 0.9
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VISION_UNTRACKED_SLOW_LEAD_FULL_MODEL_PROB = 0.97
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VISION_UNTRACKED_SLOW_LEAD_MIN_CLOSING_SPEED = 3.0
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VISION_UNTRACKED_SLOW_LEAD_MIN_CLOSING_RATIO = 0.16
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VISION_UNTRACKED_SLOW_LEAD_FULL_CLOSING_RATIO = 0.24
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VISION_UNTRACKED_SLOW_LEAD_TRIGGER_TTC = 16.0
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VISION_UNTRACKED_SLOW_LEAD_FULL_TTC = 8.0
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VISION_UNTRACKED_SLOW_LEAD_MAX_DISTANCE_TIME = 4.4
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VISION_UNTRACKED_SLOW_LEAD_MIN_DISTANCE = 80.0
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VISION_UNTRACKED_SLOW_LEAD_MAX_DISTANCE = 120.0
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VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_DISTANCE_TIME = 5.7
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VISION_UNTRACKED_SLOW_LEAD_MAX_DECEL = 0.85
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VISION_UNTRACKED_SLOW_LEAD_MIN_DECEL = 0.1
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VISION_UNTRACKED_SLOW_LEAD_RELAXED_MODEL_PROB = 0.68
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VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_LEAD_SPEED = 8.0
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VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_TTC = 10.0
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VISION_UNTRACKED_SLOW_LEAD_RELAXED_MIN_CLOSING_SPEED = 10.0
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VISION_UNTRACKED_SLOW_LEAD_RELAXED_FULL_CLOSING_SPEED = 16.0
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VISION_UNTRACKED_SLOW_LEAD_CONFIRM_TIME = 0.30
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VISION_UNTRACKED_SLOW_LEAD_IMMEDIATE_DECEL = 0.55
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VISION_UNTRACKED_SLOW_LEAD_IMMEDIATE_DISTANCE = 45.0
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VISION_UNTRACKED_SLOW_LEAD_IMMEDIATE_LEAD_BRAKE = 0.10
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VISION_SLOW_LEAD_MAX_SPEED = 5.0
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VISION_SLOW_LEAD_MIN_CLOSING_SPEED = 1.5
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VISION_SLOW_LEAD_TRIGGER_TTC = 4.5
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VISION_SLOW_LEAD_FULL_TTC = 2.0
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VISION_SLOW_LEAD_MAX_DECEL = 1.2
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VISION_SLOW_LEAD_MIN_DECEL = 0.18
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VISION_SLOW_LEAD_MIN_MODEL_PROB = 0.9
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LEAD_APPROACH_TFOLLOW_TRIGGER_TIME = 4.5
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LEAD_APPROACH_TFOLLOW_FULL_TIME = 1.5
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LEAD_APPROACH_TFOLLOW_MAX_DELTA = 0.18
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LEAD_APPROACH_TFOLLOW_MAX_CLOSING_SPEED = 6.0
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LEAD_APPROACH_TFOLLOW_MAX_LEAD_BRAKE = 2.5
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LEAD_APPROACH_TFOLLOW_MIN_CLOSING_SPEED = 0.75
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LEAD_APPROACH_TFOLLOW_MIN_LEAD_BRAKE = 0.2
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LEAD_APPROACH_TFOLLOW_WINDOW_MIN = 6.0
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LEAD_APPROACH_TFOLLOW_WINDOW_GAIN = 0.35
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LEAD_APPROACH_TFOLLOW_RATE_UP = 1.0
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LEAD_APPROACH_TFOLLOW_RATE_DOWN = 0.60
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VISION_LEAD_TFOLLOW_MAX_EXTRA_DELTA = 0.24
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VISION_LEAD_TFOLLOW_SLOW_LEAD_SPEED = 20.0
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VISION_LEAD_TFOLLOW_GAP_BUFFER_MIN = 8.0
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VISION_LEAD_TFOLLOW_GAP_BUFFER_GAIN = 0.35
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VISION_LOW_SPEED_STOP_BUFFER_MAX_EGO_SPEED = 6.5
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VISION_LOW_SPEED_STOP_BUFFER_MAX_LEAD_SPEED = 3.25
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VISION_LOW_SPEED_STOP_BUFFER_HOLD_MAX_LEAD_SPEED = 4.0
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VISION_LOW_SPEED_STOP_BUFFER_MIN_MODEL_PROB = 0.9
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VISION_LOW_SPEED_STOP_BUFFER_MIN_CLOSING_SPEED = 0.35
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VISION_LOW_SPEED_STOP_BUFFER_MIN_HOLD_REL_SPEED = -0.2
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VISION_LOW_SPEED_STOP_BUFFER_BASE = 3.8
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VISION_LOW_SPEED_STOP_BUFFER_EGO_GAIN = 0.80
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VISION_LOW_SPEED_STOP_BUFFER_LEAD_GAIN = 0.25
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VISION_LOW_SPEED_STOP_BUFFER_RELEASE_MARGIN = 0.9
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VISION_LOW_SPEED_STOP_BUFFER_HOLD_TIME = 0.8
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VISION_LOW_SPEED_STOP_BUFFER_MIN_BRAKE = 1.25
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VISION_LOW_SPEED_STOP_BUFFER_BRAKE_GAIN = 0.25
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VISION_CLOSE_STOP_HOLD_MAX_EGO_SPEED = 0.75
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VISION_CLOSE_STOP_HOLD_MAX_LEAD_SPEED = 0.8
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VISION_CLOSE_STOP_HOLD_MAX_DISTANCE = 3.5
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VISION_CLOSE_STOP_HOLD_MIN_MODEL_PROB = 0.95
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VISION_CLOSE_STOP_HOLD_MIN_BRAKE = 0.20
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VISION_CLOSE_STOP_HOLD_MAX_BRAKE = 0.36
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VISION_CLOSE_SETTLE_MAX_EGO_SPEED = 0.75
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VISION_CLOSE_SETTLE_MAX_LEAD_SPEED = 2.75
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VISION_CLOSE_SETTLE_MAX_DISTANCE = 4.2
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VISION_CLOSE_SETTLE_MAX_LEAD_DELTA = 2.6
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VISION_CLOSE_SETTLE_MIN_BRAKE = 0.16
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VISION_CLOSE_SETTLE_MAX_BRAKE = 0.30
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VISION_CLOSE_FINAL_GUARD_MAX_EGO_SPEED = 0.5
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VISION_CLOSE_FINAL_GUARD_MAX_LEAD_SPEED = 2.75
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VISION_CLOSE_FINAL_GUARD_MAX_DISTANCE = 4.5
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VISION_CLOSE_FINAL_GUARD_MIN_BRAKE = 0.18
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VISION_CLOSE_FINAL_GUARD_MAX_BRAKE = 0.28
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VISION_CLOSE_RELEASE_HOLD_MAX_EGO_SPEED = 2.5
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VISION_CLOSE_RELEASE_HOLD_MAX_LEAD_SPEED = 3.5
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VISION_CLOSE_RELEASE_HOLD_MAX_DISTANCE = 4.2
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VISION_CLOSE_RELEASE_HOLD_MIN_MODEL_PROB = 0.98
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VISION_CLOSE_RELEASE_HOLD_MIN_LEAD_DELTA = -0.1
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VISION_CLOSE_RELEASE_HOLD_MAX_LEAD_DELTA = 1.5
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VISION_CLOSE_RELEASE_HOLD_MIN_BRAKE = 0.18
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VISION_CLOSE_RELEASE_HOLD_MAX_BRAKE = 0.40
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MANUAL_STOP_RESUME_OVERRIDE_TIME = 3.0
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MANUAL_STOP_RESUME_OVERRIDE_MAX_SPEED = 2.0
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MANUAL_STOP_RESUME_OVERRIDE_MIN_ACCEL = 0.2
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FORCE_STOP_HANDOFF_MAX_VCRUISE = 0.5
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LEAD_CATCHUP_ACCEL_MIN_EGO = 8.0
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LEAD_CATCHUP_ACCEL_MIN_LEAD_DELTA = -0.5
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LEAD_CATCHUP_ACCEL_MAX_GAP_BUFFER_MIN = 4.0
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LEAD_CATCHUP_ACCEL_MAX_GAP_BUFFER_GAIN = 0.15
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RADAR_MATCHED_FOLLOW_CATCHUP_CAP_BUFFER_MARGIN = 0.75
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RADAR_MATCHED_FOLLOW_CATCHUP_HOLD_CAP = 0.04
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RADAR_MATCHED_FOLLOW_CATCHUP_HOLD_MAX_GAP_ERROR = 0.75
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POST_DEPARTURE_FOLLOW_BYPASS_MIN_SPEED = 12.0
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POST_DEPARTURE_FOLLOW_BYPASS_MIN_MODEL_PROB = 0.95
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POST_DEPARTURE_FOLLOW_BYPASS_MIN_LEAD_DELTA = 0.35
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POST_DEPARTURE_FOLLOW_BYPASS_MIN_LEAD_ACCEL = 0.25
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POST_DEPARTURE_FOLLOW_BYPASS_MIN_HEADWAY_MARGIN = 0.10
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POST_DEPARTURE_FOLLOW_SETTLE_LATCH_TIME = 75.0
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POST_DEPARTURE_FOLLOW_SETTLE_MIN_SPEED = 8.0
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POST_DEPARTURE_FOLLOW_SETTLE_MIN_MODEL_PROB = 0.9
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POST_DEPARTURE_FOLLOW_SETTLE_MAX_LATERAL_OFFSET = 1.15
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POST_DEPARTURE_FOLLOW_SETTLE_MAX_CLOSING_SPEED = 0.8
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POST_DEPARTURE_FOLLOW_SETTLE_MAX_LEAD_BRAKE = 0.10
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POST_DEPARTURE_FOLLOW_SETTLE_MIN_HEADWAY_MARGIN = 0.10
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POST_DEPARTURE_FOLLOW_SETTLE_COMPLETE_HEADWAY_MARGIN = 0.05
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COMFORTABLE_PULLAWAY_FOLLOW_MIN_MODEL_PROB = 0.95
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COMFORTABLE_PULLAWAY_FOLLOW_MIN_LEAD_DELTA = -0.05
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COMFORTABLE_PULLAWAY_FOLLOW_MIN_LEAD_ACCEL = 0.20
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COMFORTABLE_PULLAWAY_FOLLOW_MIN_HEADWAY_MARGIN = 0.20
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SPACIOUS_TRACKED_FOLLOW_MIN_MODEL_PROB = 0.98
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SPACIOUS_TRACKED_FOLLOW_MIN_HEADWAY_MARGIN = 0.45
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SPACIOUS_TRACKED_FOLLOW_MAX_CLOSING_SPEED = 0.60
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SPACIOUS_TRACKED_FOLLOW_MAX_LEAD_BRAKE = 0.10
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SPACIOUS_TRACKED_FOLLOW_LATCH_TIME = 1.25
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SPACIOUS_TRACKED_FOLLOW_LATCH_MIN_LEAD_DELTA = 0.90
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LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_SPEED = 12.0
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LOW_SPEED_FOLLOW_ACCEL_CAP_MIN_MODEL_PROB = 0.85
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LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_LEAD_BRAKE = 0.20
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LOW_SPEED_FOLLOW_ACCEL_CAP_MIN_LEAD_DELTA = -1.2
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LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_GAP_BUFFER_MIN = 6.0
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LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_GAP_BUFFER_GAIN = 0.35
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LOW_SPEED_FOLLOW_TRANSITION_MIN_SPEED = 3.0
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LOW_SPEED_FOLLOW_TRANSITION_MAX_SPEED = 12.0
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LOW_SPEED_FOLLOW_TRANSITION_MIN_MODEL_PROB = 0.85
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LOW_SPEED_FOLLOW_TRANSITION_MAX_LEAD_BRAKE = 0.20
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LOW_SPEED_FOLLOW_TRANSITION_MIN_GAP_MARGIN = 1.0
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LOW_SPEED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED = 0.6
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LOW_SPEED_FOLLOW_TRANSITION_PREV_ACCEL_MIN = 0.18
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LOW_SPEED_FOLLOW_TRANSITION_TARGET_BRAKE_MIN = -0.18
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LOW_SPEED_FOLLOW_TRANSITION_MAX_BRAKE = 0.14
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LOW_SPEED_FOLLOW_TRANSITION_MIN_BRAKE = 0.08
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MIN_SPEED = 10.0
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_SPEED = 20.0
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MIN_MODEL_PROB = 0.85
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LEAD_BRAKE = 0.25
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_PULLAWAY_SPEED = 1.0
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_GAP_BUFFER_MIN = 12.0
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_GAP_BUFFER_GAIN = 0.9
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CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET = 1.15
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CRUISE_TRACKED_LEAD_ACCEL_CAP_UNRESOLVED_MIN_CLOSING_SPEED = 1.5
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CRUISE_TRACKED_LEAD_ACCEL_CAP_UNRESOLVED_MAX_LEAD_DELTA = 0.25
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CRUISE_TRACKED_LEAD_ACCEL_CAP_TRACKING_ONLY_MAX_HEADWAY_ABOVE_TARGET = 0.95
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_TRACKING_ONLY_MAX_CLOSING_SPEED = 0.8
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_TRACKING_ONLY_MAX_LEAD_BRAKE = 0.10
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_ACCEL = 0.18
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_ACCEL_AWAY_MIN = 0.25
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_ACCEL_AWAY_MIN_LEAD_DELTA = 0.35
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_ACCEL_AWAY_MIN_GAP_MARGIN = 1.0
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_SPEED = 12.0
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_SPEED = 22.0
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_MODEL_PROB = 0.9
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_LEAD_BRAKE = 0.35
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_LATERAL_OFFSET = 1.15
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_PULLAWAY_SPEED = 2.25
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_HEADWAY_ABOVE_TARGET = 0.95
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_DELTA_A = 0.18
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_STEP = 0.06
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_STEP = 0.18
|
||
|
||
# Uncertainty-based filter disable thresholds
|
||
UNCERT_SLOPE_TRIG = 0.12 # per second
|
||
UNCERT_MAG_TRIG = 0.50
|
||
UNCERT_PANIC_MIN_CLOSING_SPEED = 2.0
|
||
UNCERT_PANIC_MIN_CLOSING_SPEED_GAIN = 0.08
|
||
UNCERT_PANIC_MAX_GAP_BUFFER_MIN = 8.0
|
||
UNCERT_PANIC_MAX_GAP_BUFFER_GAIN = 0.35
|
||
STEADY_FOLLOW_SMOOTHING_MIN_SPEED = 22.0
|
||
STEADY_FOLLOW_SMOOTHING_MIN_CLOSING_SPEED = 0.15
|
||
STEADY_FOLLOW_SMOOTHING_MAX_CLOSING_SPEED = 1.8
|
||
STEADY_FOLLOW_SMOOTHING_MIN_HEADWAY = 0.95
|
||
STEADY_FOLLOW_SMOOTHING_HEADWAY_BELOW_TARGET = 0.35
|
||
STEADY_FOLLOW_SMOOTHING_HEADWAY_ABOVE_TARGET = 0.90
|
||
STEADY_FOLLOW_SMOOTHING_MAX_LEAD_BRAKE = 0.35
|
||
STEADY_FOLLOW_SMOOTHING_MIN_MODEL_PROB = 0.7
|
||
STEADY_FOLLOW_SMOOTHING_FILTER_FACTOR_FLOOR = 0.24
|
||
STEADY_FOLLOW_BRAKE_CAP_MIN_HEADWAY = 1.05
|
||
STEADY_FOLLOW_BRAKE_CAP_MAX_HEADWAY_ABOVE_TARGET = 0.90
|
||
STEADY_FOLLOW_BRAKE_CAP_MIN_REL_SPEED = -1.2
|
||
STEADY_FOLLOW_BRAKE_CAP_MAX_CLOSING_SPEED = 2.2
|
||
STEADY_FOLLOW_BRAKE_CAP_ZERO_REL_SPEED_DECEL = 0.12
|
||
STEADY_FOLLOW_BRAKE_CAP_OPENING_DECEL = 0.08
|
||
STEADY_FOLLOW_BRAKE_CAP_MIN_DECEL = 0.18
|
||
STEADY_FOLLOW_BRAKE_CAP_MAX_DECEL = 0.32
|
||
STEADY_FOLLOW_BRAKE_CAP_SPACIOUS_HEADWAY_MARGIN = 0.45
|
||
STEADY_FOLLOW_BRAKE_CAP_SPACIOUS_MIN_HEADWAY = 1.80
|
||
STEADY_FOLLOW_BRAKE_CAP_SPACIOUS_MAX_LEAD_BRAKE = 0.15
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MIN_DISTANCE = 80.0
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MIN_CLOSING_SPEED = 0.1
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MAX_CLOSING_SPEED = 2.0
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MIN_MODEL_PROB = 0.95
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MAX_LEAD_BRAKE = 0.12
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MIN_TTC = 7.5
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MIN_HEADWAY_MARGIN = 0.55
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_FULL_HEADWAY_MARGIN = 1.00
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MIN_DECEL = 0.05
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_MAX_DECEL = 0.18
|
||
FAR_LEAD_COMFORT_BRAKE_CAP_FULL_RELAX_DECEL = 0.05
|
||
MATCHED_FOLLOW_TRANSITION_MIN_SPEED = 20.0
|
||
MATCHED_FOLLOW_TRANSITION_MIN_HEADWAY_MARGIN = 0.25
|
||
MATCHED_FOLLOW_TRANSITION_FULL_HEADWAY_MARGIN = 0.75
|
||
MATCHED_FOLLOW_TRANSITION_MIN_MODEL_PROB = 0.9
|
||
MATCHED_FOLLOW_TRANSITION_MAX_LEAD_BRAKE = 0.18
|
||
MATCHED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED = 1.75
|
||
MATCHED_FOLLOW_TRANSITION_MIN_TTC = 12.0
|
||
MATCHED_FOLLOW_TRANSITION_MIN_POSITIVE_STEP = 0.08
|
||
MATCHED_FOLLOW_TRANSITION_MAX_POSITIVE_STEP = 0.18
|
||
MATCHED_FOLLOW_TRANSITION_MIN_NEGATIVE_STEP = 0.08
|
||
MATCHED_FOLLOW_TRANSITION_MAX_NEGATIVE_STEP = 0.16
|
||
MATCHED_FOLLOW_TRANSITION_SIGN_CROSS_STEP = 0.10
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_SPEED = 10.0
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_SPEED = MATCHED_FOLLOW_TRANSITION_MIN_SPEED
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_HEADWAY_MARGIN = 0.45
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_FULL_HEADWAY_MARGIN = 1.00
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_MODEL_PROB = 0.98
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_LEAD_BRAKE = 0.08
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED = 1.25
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TTC = 18.0
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_POSITIVE_STEP = 0.06
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_POSITIVE_STEP = 0.10
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_NEGATIVE_STEP = 0.05
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_NEGATIVE_STEP = 0.08
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_SIGN_CROSS_STEP = 0.06
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TARGET = -0.12
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_DELTA_A = 0.12
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_SPEED = 3.0
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_SPEED = 35.0
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_MODEL_PROB = 0.95
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_LEAD_BRAKE = 0.80
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_CLOSING_SPEED = 3.25
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_TTC = 6.0
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_HEADWAY_MARGIN = 0.25
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_FULL_HEADWAY_MARGIN = 0.85
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_DELTA_A = 0.08
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_DEADBAND = 0.04
|
||
MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_DEADBAND = 0.08
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MIN_SPEED = 20.0
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MIN_MODEL_PROB = 0.95
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MAX_LEAD_BRAKE = 0.35
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MAX_CLOSING_SPEED = 3.5
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MIN_TTC = 8.0
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MIN_HEADWAY_BELOW_TARGET = 0.45
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MAX_HEADWAY_ABOVE_TARGET = 0.85
|
||
LOW_SPEED_IDENTICAL_RADAR_DUPLICATE_TRANSITION_EXTRA_HEADWAY = 0.15
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_MIN_DELTA_A = 0.35
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_POSITIVE_STEP = 0.22
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_NEGATIVE_STEP = 0.32
|
||
NEAR_DUPLICATE_LEAD_TRANSITION_SIGN_CROSS_STEP = 0.18
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_SPEED = 12.0
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_MODEL_PROB = 0.95
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_PREV_DECEL = 0.35
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_CLOSING_SPEED = 0.5
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_LEAD_BRAKE = 0.8
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_HEADWAY_ABOVE_TARGET = 0.85
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_DELTA_A = 0.35
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_DREL_DIFF = 1.5
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_VREL_DIFF = 0.35
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_POSITIVE_STEP = 0.12
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_POSITIVE_STEP = 0.28
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_SIGN_CROSS_STEP = 0.22
|
||
TRACKED_VISION_MODEL_FLOOR_MIN_SPEED = 10.0
|
||
TRACKED_VISION_MODEL_FLOOR_MIN_MODEL_PROB = 0.95
|
||
TRACKED_VISION_MODEL_FLOOR_MIN_MODEL_DECEL = 0.80
|
||
TRACKED_VISION_MODEL_FLOOR_MIN_CLOSING_SPEED = 1.0
|
||
TRACKED_VISION_MODEL_FLOOR_MAX_TTC = 22.0
|
||
TRACKED_VISION_MODEL_FLOOR_MIN_GAP_MARGIN = -2.0
|
||
TRACKED_VISION_MODEL_FLOOR_MAX_GAP_BUFFER_MIN = 4.0
|
||
TRACKED_VISION_MODEL_FLOOR_MAX_GAP_BUFFER_GAIN = 0.25
|
||
TRACKED_VISION_MODEL_FLOOR_MIN_DECEL = 0.35
|
||
TRACKED_VISION_MODEL_FLOOR_MAX_DECEL = 1.10
|
||
TRACKED_VISION_MODEL_FLOOR_LEAD_BRAKE_MAX = 0.18
|
||
TRACKED_VISION_MODEL_CAP_MIN_SPEED = 14.0
|
||
TRACKED_VISION_MODEL_CAP_MAX_SPEED = 32.0
|
||
TRACKED_VISION_MODEL_CAP_MIN_MODEL_PROB = 0.95
|
||
TRACKED_VISION_MODEL_CAP_MAX_MODEL_DECEL = 0.75
|
||
TRACKED_VISION_MODEL_CAP_MIN_CLOSING_SPEED = 0.75
|
||
TRACKED_VISION_MODEL_CAP_MAX_CLOSING_SPEED = 4.0
|
||
TRACKED_VISION_MODEL_CAP_MAX_LEAD_BRAKE = 0.55
|
||
TRACKED_VISION_MODEL_CAP_MIN_TTC = 7.0
|
||
TRACKED_VISION_MODEL_CAP_MIN_GAP_MARGIN = -1.5
|
||
TRACKED_VISION_MODEL_CAP_MAX_GAP_BUFFER_MIN = 4.0
|
||
TRACKED_VISION_MODEL_CAP_MAX_GAP_BUFFER_GAIN = 0.25
|
||
TRACKED_VISION_MODEL_CAP_MIN_DECEL = 0.45
|
||
TRACKED_VISION_MODEL_CAP_MAX_DECEL = 1.10
|
||
|
||
# Lookup table for turns
|
||
_A_TOTAL_MAX_V = [3.5, 3.5, 3.2]
|
||
_A_TOTAL_MAX_BP = [0., 20., 40.]
|
||
|
||
_preap_follow_cache = None
|
||
|
||
|
||
def get_preap_follow_limit(v_ego):
|
||
global _preap_follow_cache
|
||
if _preap_follow_cache is None:
|
||
try:
|
||
from opendbc.car.tesla.preap.constants import ACCEL_PREAP_BP, ACCEL_PREAP_FOLLOW
|
||
_preap_follow_cache = (ACCEL_PREAP_BP, ACCEL_PREAP_FOLLOW)
|
||
except ImportError:
|
||
_preap_follow_cache = (None, None)
|
||
bp, values = _preap_follow_cache
|
||
if bp is None:
|
||
return None
|
||
return float(np.interp(v_ego, bp, values))
|
||
|
||
|
||
def get_longitudinal_personality(sm):
|
||
return sm['selfdriveState'].personality
|
||
|
||
|
||
def get_max_accel(v_ego):
|
||
return np.interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
|
||
|
||
def get_coast_accel(pitch):
|
||
return np.sin(pitch) * -5.65 - 0.3 # fitted from data using xx/projects/allow_throttle/compute_coast_accel.py
|
||
|
||
|
||
def limit_accel_in_turns(v_ego, angle_steers, a_target, CP):
|
||
"""
|
||
This function returns a limited long acceleration allowed, depending on the existing lateral acceleration
|
||
this should avoid accelerating when losing the target in turns
|
||
"""
|
||
# FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel
|
||
# The lookup table for turns should also be updated if we do this
|
||
a_total_max = np.interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
|
||
a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
|
||
a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))
|
||
|
||
return [a_target[0], min(a_target[1], a_x_allowed)]
|
||
|
||
|
||
def should_publish_planner_fcw(crash_cnt: int, car_state, radar_state) -> bool:
|
||
return (
|
||
crash_cnt > 2 and
|
||
not car_state.standstill and
|
||
should_trigger_planner_fcw(radar_state.leadOne, float(car_state.vEgo))
|
||
)
|
||
|
||
|
||
def get_vehicle_min_accel(CP, v_ego):
|
||
# Planner-side physical decel capability estimate for GM pedal-long paths.
|
||
if getattr(CP, "carName", "") == "gm" and getattr(CP, "enableGasInterceptorDEPRECATED", False):
|
||
try:
|
||
from opendbc.car.gm.values import GMFlags, CAR
|
||
if bool(CP.flags & GMFlags.PEDAL_LONG.value):
|
||
bolt_pedal_long_cars = {
|
||
CAR.CHEVROLET_BOLT_CC_2017,
|
||
CAR.CHEVROLET_BOLT_CC_2018_2021,
|
||
CAR.CHEVROLET_BOLT_ACC_2022_2023_PEDAL,
|
||
CAR.CHEVROLET_BOLT_CC_2022_2023,
|
||
CAR.CHEVROLET_MALIBU_HYBRID_CC,
|
||
}
|
||
if CP.carFingerprint in bolt_pedal_long_cars:
|
||
return float(np.interp(v_ego, [0.0, 1.5, 4.0, 8.0, 15.0, 30.0],
|
||
[-0.93, -1.28, -1.98, -2.58, -2.86, -2.95]))
|
||
return float(np.interp(v_ego, [0.0, 1.5, 4.0, 8.0, 15.0, 30.0],
|
||
[-0.95, -1.3, -1.85, -2.3, -2.6, -2.8]))
|
||
except Exception:
|
||
pass
|
||
return float(ACCEL_MIN)
|
||
|
||
|
||
def get_planner_v_ego(CP, car_state):
|
||
v_ego = max(car_state.vEgo, car_state.vEgoCluster)
|
||
|
||
is_gm = getattr(CP, "carName", "") == "gm" or getattr(CP, "brand", "") == "gm"
|
||
if is_gm and getattr(CP, "enableGasInterceptorDEPRECATED", False):
|
||
try:
|
||
from opendbc.car.gm.values import GMFlags
|
||
is_gm_pedal_long = bool(CP.flags & GMFlags.PEDAL_LONG.value)
|
||
if is_gm_pedal_long:
|
||
return float(car_state.vEgo)
|
||
except Exception:
|
||
pass
|
||
|
||
return float(v_ego)
|
||
|
||
|
||
def get_accel_from_plan_classic(CP, speeds, accels, vEgoStopping):
|
||
if len(speeds) == CONTROL_N:
|
||
v_target_now = np.interp(DT_MDL, CONTROL_N_T_IDX, speeds)
|
||
a_target_now = np.interp(DT_MDL, CONTROL_N_T_IDX, accels)
|
||
|
||
v_target = np.interp(CP.longitudinalActuatorDelay + DT_MDL, CONTROL_N_T_IDX, speeds)
|
||
if v_target != v_target_now:
|
||
a_target = 2 * (v_target - v_target_now) / CP.longitudinalActuatorDelay - a_target_now
|
||
else:
|
||
a_target = a_target_now
|
||
|
||
v_target_1sec = np.interp(CP.longitudinalActuatorDelay + DT_MDL + 1.0, CONTROL_N_T_IDX, speeds)
|
||
else:
|
||
v_target = 0.0
|
||
v_target_1sec = 0.0
|
||
a_target = 0.0
|
||
should_stop = (v_target < vEgoStopping and
|
||
v_target_1sec < vEgoStopping)
|
||
return a_target, should_stop
|
||
|
||
|
||
def get_accel_from_plan(speeds, accels, action_t=DT_MDL, vEgoStopping=0.05):
|
||
if len(speeds) == CONTROL_N:
|
||
v_now = speeds[0]
|
||
a_now = accels[0]
|
||
|
||
v_target = np.interp(action_t, CONTROL_N_T_IDX, speeds)
|
||
a_target = 2 * (v_target - v_now) / (action_t) - a_now
|
||
v_target_1sec = np.interp(action_t + 1.0, CONTROL_N_T_IDX, speeds)
|
||
else:
|
||
v_target = 0.0
|
||
v_target_1sec = 0.0
|
||
a_target = 0.0
|
||
should_stop = (v_target < vEgoStopping and
|
||
v_target_1sec < vEgoStopping)
|
||
return a_target, should_stop
|
||
|
||
|
||
class LongitudinalPlanner:
|
||
def __init__(self, CP, init_v=0.0, init_a=0.0, dt=DT_MDL):
|
||
self.CP = CP
|
||
self.mpc = LongitudinalMpc(dt=dt)
|
||
self.fcw = False
|
||
self.dt = dt
|
||
self.model_allow_throttle = True
|
||
self.allow_throttle = True
|
||
self.mode = 'acc'
|
||
self.is_preap = (
|
||
CP.brand == "tesla" and CP.carFingerprint == "TESLA_MODEL_S_PREAP" and
|
||
CP.openpilotLongitudinalControl and not CP.pcmCruise
|
||
)
|
||
self.nap_adaptive_accel = False
|
||
self._preap_params = None
|
||
self._preap_param_frame = 0
|
||
|
||
self.generation = None
|
||
|
||
self.a_desired = init_a
|
||
self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
|
||
self.v_model_error = 0.0
|
||
self.output_a_target = 0.0
|
||
self.output_should_stop = False
|
||
self.confident_lead_depart_elapsed = 0.0
|
||
|
||
self.v_desired_trajectory = np.zeros(CONTROL_N)
|
||
self.a_desired_trajectory = np.zeros(CONTROL_N)
|
||
self.j_desired_trajectory = np.zeros(CONTROL_N)
|
||
self.solverExecutionTime = 0.0
|
||
|
||
# ---- Rubberband mitigation state ----
|
||
# Two uncertainty tracks (slow/fast) for asymmetric gating
|
||
self.uncert_slow = FirstOrderFilter(0.0, 1.6, self.dt) # ~lam=0.6
|
||
self.uncert_fast = FirstOrderFilter(0.0, 0.9, self.dt) # faster cool-down for accel decisions
|
||
# Lead stability tracking
|
||
self.prev_lead_dist = None
|
||
self.last_big_brake_t = 0.0
|
||
self.stable_lead = False
|
||
# Smoothed lead distance
|
||
self.lead_dist_f = None
|
||
|
||
# Uncertainty slope tracking
|
||
self._uncert_last = 0.0
|
||
self._uncert_last_t = None
|
||
self._panic_bypass_log_t = 0.0
|
||
self.effective_t_follow = None
|
||
self.vision_low_speed_stop_hold_until = 0.0
|
||
self.vision_lead_approach_confirm_t = 0.0
|
||
self.untracked_slow_lead_confirm_t = 0.0
|
||
self.manual_stop_resume_override_until = 0.0
|
||
self.lead_depart_accel_hold_until = 0.0
|
||
self.spacious_follow_cap_bypass_until = 0.0
|
||
self.post_departure_follow_settle_until = 0.0
|
||
|
||
if self.is_preap:
|
||
try:
|
||
from openpilot.common.params import Params
|
||
self._preap_params = Params()
|
||
self.nap_adaptive_accel = self._preap_params.get_bool("NAPAdaptiveAccel")
|
||
except Exception:
|
||
self._preap_params = None
|
||
self.nap_adaptive_accel = False
|
||
|
||
@property
|
||
def mlsim(self):
|
||
return is_tinygrad_model_version(self.generation)
|
||
|
||
def get_mpc_mode(self) -> str:
|
||
if not self.mlsim:
|
||
return self.mode
|
||
return getattr(self.mpc, 'mode', 'acc')
|
||
|
||
@staticmethod
|
||
def get_model_speed_error(model_msg, v_ego):
|
||
try:
|
||
if len(model_msg.temporalPose.trans):
|
||
return float(np.clip(model_msg.temporalPose.trans[0] - v_ego, -5.0, 5.0))
|
||
except AttributeError:
|
||
pass
|
||
return 0.0
|
||
|
||
@staticmethod
|
||
def parse_model(model_msg, model_error, v_ego, starpilot_toggles):
|
||
if (len(model_msg.position.x) == ModelConstants.IDX_N and
|
||
len(model_msg.velocity.x) == ModelConstants.IDX_N and
|
||
len(model_msg.acceleration.x) == ModelConstants.IDX_N):
|
||
x = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC
|
||
v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x) - model_error
|
||
a = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.acceleration.x)
|
||
j = np.zeros(len(T_IDXS_MPC))
|
||
else:
|
||
x = np.zeros(len(T_IDXS_MPC))
|
||
v = np.zeros(len(T_IDXS_MPC))
|
||
a = np.zeros(len(T_IDXS_MPC))
|
||
j = np.zeros(len(T_IDXS_MPC))
|
||
|
||
if starpilot_toggles.taco_tune:
|
||
max_lat_accel = np.interp(v_ego, [5, 10, 20], [1.5, 2.0, 3.0])
|
||
curvatures = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.orientationRate.z) / np.clip(v, 0.3, 100.0)
|
||
max_v = np.sqrt(max_lat_accel / (np.abs(curvatures) + 1e-3)) - 2.0
|
||
v = np.minimum(max_v, v)
|
||
|
||
if len(model_msg.meta.disengagePredictions.gasPressProbs) > 1:
|
||
throttle_prob = model_msg.meta.disengagePredictions.gasPressProbs[1]
|
||
else:
|
||
throttle_prob = 1.0
|
||
return x, v, a, j, throttle_prob
|
||
|
||
def get_close_lead_brake_cap(self, lead, v_ego, accel_min):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(lead.aLeadK))
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
closing_speed = max(0.0, v_ego - lead.vLead)
|
||
projected_closing_speed = closing_speed + lead_brake * reaction_t
|
||
if projected_closing_speed < 0.1 and lead_brake < 0.5:
|
||
return None
|
||
|
||
target_gap = float(np.clip(2.0 + 0.2 * v_ego, 2.0, 6.0))
|
||
delay_buffer = projected_closing_speed * reaction_t
|
||
available_gap = max(float(lead.dRel) - target_gap - delay_buffer, 0.5)
|
||
projected_ttc = available_gap / max(projected_closing_speed, 0.1)
|
||
if projected_ttc > CLOSE_LEAD_BRAKE_CAP_MAX_TTC:
|
||
return None
|
||
required_decel = (projected_closing_speed ** 2) / (2.0 * available_gap) + 0.7 * lead_brake
|
||
if required_decel < 0.2:
|
||
return None
|
||
|
||
return max(accel_min, -required_decel)
|
||
|
||
def get_vision_lead_approach_cap(self, lead, v_ego, accel_min, t_follow):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < VISION_LEAD_APPROACH_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(lead.aLeadK))
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
closing_speed = max(0.0, v_ego - lead.vLead)
|
||
projected_closing_speed = closing_speed + lead_brake * reaction_t
|
||
if projected_closing_speed < VISION_LEAD_APPROACH_MIN_CLOSING_SPEED:
|
||
return None
|
||
|
||
tight_follow_gap = float(t_follow * v_ego + VISION_LEAD_APPROACH_TIGHT_BUFFER)
|
||
gap_to_tight_follow = float(lead.dRel) - tight_follow_gap
|
||
time_to_tight_follow = gap_to_tight_follow / max(projected_closing_speed, 0.1)
|
||
if time_to_tight_follow > VISION_LEAD_APPROACH_TRIGGER_TIME:
|
||
return None
|
||
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
|
||
if float(lead.dRel) > desired_gap + VISION_LEAD_APPROACH_TIGHT_BUFFER:
|
||
return None
|
||
|
||
time_factor = float(np.clip((VISION_LEAD_APPROACH_TRIGGER_TIME - time_to_tight_follow) /
|
||
(VISION_LEAD_APPROACH_TRIGGER_TIME - VISION_LEAD_APPROACH_FULL_TIME), 0.0, 1.0))
|
||
prob_factor = float(np.clip((lead_prob - VISION_LEAD_APPROACH_MIN_MODEL_PROB) /
|
||
(VISION_LEAD_APPROACH_FULL_MODEL_PROB - VISION_LEAD_APPROACH_MIN_MODEL_PROB), 0.0, 1.0))
|
||
closing_factor = float(np.clip(projected_closing_speed / (VISION_LEAD_APPROACH_MIN_CLOSING_SPEED + 2.5), 0.0, 1.0))
|
||
tight_follow_deficit = max(tight_follow_gap - float(lead.dRel), 0.0)
|
||
tight_follow_buffer = max(VISION_LEAD_APPROACH_DEFICIT_BUFFER_MIN,
|
||
VISION_LEAD_APPROACH_DEFICIT_BUFFER_GAIN * float(v_ego) + 1.0)
|
||
deficit_factor = float(np.clip(tight_follow_deficit / tight_follow_buffer, 0.0, 1.0))
|
||
|
||
approach_decel = VISION_LEAD_APPROACH_MAX_DECEL * time_factor * (0.45 + 0.55 * prob_factor)
|
||
approach_decel *= 0.6 + 0.4 * closing_factor
|
||
deficit_decel = VISION_LEAD_APPROACH_DEFICIT_MAX_DECEL * deficit_factor * prob_factor
|
||
deficit_decel *= 0.5 + 0.5 * closing_factor
|
||
approach_decel = max(approach_decel, deficit_decel)
|
||
|
||
# If a tracked vision lead is already far inside the tight-follow window and
|
||
# it is actively braking, don't stay stuck at the softer comfort cap.
|
||
if deficit_factor >= VISION_LEAD_APPROACH_BRAKING_DEFICIT_MIN and lead_brake >= VISION_LEAD_APPROACH_BRAKING_MIN_LEAD_BRAKE:
|
||
braking_floor = float(np.interp(
|
||
lead_brake,
|
||
[VISION_LEAD_APPROACH_BRAKING_MIN_LEAD_BRAKE, VISION_LEAD_APPROACH_BRAKING_FULL_LEAD_BRAKE],
|
||
[VISION_LEAD_APPROACH_BRAKING_FLOOR_MIN_DECEL, VISION_LEAD_APPROACH_BRAKING_FLOOR_MAX_DECEL],
|
||
))
|
||
braking_floor *= 0.85 + 0.15 * max(closing_factor, prob_factor)
|
||
approach_decel = max(approach_decel, braking_floor)
|
||
|
||
if approach_decel < VISION_LEAD_APPROACH_MIN_DECEL:
|
||
return None
|
||
|
||
return max(accel_min, -approach_decel)
|
||
|
||
def get_vision_untracked_slow_lead_cap(self, lead, v_ego, accel_min):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
|
||
lead_brake = max(0.0, -float(lead.aLeadK))
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
closing_speed = max(0.0, v_ego - lead.vLead)
|
||
projected_closing_speed = closing_speed + lead_brake * reaction_t
|
||
if projected_closing_speed < VISION_UNTRACKED_SLOW_LEAD_MIN_CLOSING_SPEED:
|
||
return None
|
||
|
||
closing_ratio = projected_closing_speed / max(float(v_ego), 0.1)
|
||
if closing_ratio < VISION_UNTRACKED_SLOW_LEAD_MIN_CLOSING_RATIO:
|
||
return None
|
||
|
||
projected_ttc = float(lead.dRel) / max(projected_closing_speed, 0.1)
|
||
if projected_ttc > VISION_UNTRACKED_SLOW_LEAD_TRIGGER_TTC:
|
||
return None
|
||
|
||
min_model_prob = VISION_UNTRACKED_SLOW_LEAD_MIN_MODEL_PROB
|
||
max_distance_time = VISION_UNTRACKED_SLOW_LEAD_MAX_DISTANCE_TIME
|
||
if float(lead.vLead) <= VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_LEAD_SPEED and \
|
||
projected_ttc <= VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_TTC:
|
||
closing_relax = float(np.clip((projected_closing_speed - VISION_UNTRACKED_SLOW_LEAD_RELAXED_MIN_CLOSING_SPEED) /
|
||
(VISION_UNTRACKED_SLOW_LEAD_RELAXED_FULL_CLOSING_SPEED -
|
||
VISION_UNTRACKED_SLOW_LEAD_RELAXED_MIN_CLOSING_SPEED), 0.0, 1.0))
|
||
ttc_relax = float(np.clip((VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_TTC - projected_ttc) /
|
||
(VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_TTC -
|
||
VISION_UNTRACKED_SLOW_LEAD_FULL_TTC), 0.0, 1.0))
|
||
relax_factor = closing_relax * ttc_relax
|
||
min_model_prob = float(np.interp(relax_factor, [0.0, 1.0],
|
||
[VISION_UNTRACKED_SLOW_LEAD_MIN_MODEL_PROB,
|
||
VISION_UNTRACKED_SLOW_LEAD_RELAXED_MODEL_PROB]))
|
||
max_distance_time = float(np.interp(relax_factor, [0.0, 1.0],
|
||
[VISION_UNTRACKED_SLOW_LEAD_MAX_DISTANCE_TIME,
|
||
VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_DISTANCE_TIME]))
|
||
|
||
max_distance = float(np.clip(max_distance_time * v_ego,
|
||
VISION_UNTRACKED_SLOW_LEAD_MIN_DISTANCE,
|
||
VISION_UNTRACKED_SLOW_LEAD_MAX_DISTANCE))
|
||
if float(lead.dRel) > max_distance:
|
||
return None
|
||
|
||
if lead_prob < min_model_prob:
|
||
return None
|
||
|
||
time_factor = float(np.clip((VISION_UNTRACKED_SLOW_LEAD_TRIGGER_TTC - projected_ttc) /
|
||
(VISION_UNTRACKED_SLOW_LEAD_TRIGGER_TTC - VISION_UNTRACKED_SLOW_LEAD_FULL_TTC),
|
||
0.0, 1.0))
|
||
prob_factor = float(np.clip((lead_prob - min_model_prob) /
|
||
(VISION_UNTRACKED_SLOW_LEAD_FULL_MODEL_PROB - min_model_prob),
|
||
0.0, 1.0))
|
||
closing_factor = float(np.clip((closing_ratio - VISION_UNTRACKED_SLOW_LEAD_MIN_CLOSING_RATIO) /
|
||
(VISION_UNTRACKED_SLOW_LEAD_FULL_CLOSING_RATIO - VISION_UNTRACKED_SLOW_LEAD_MIN_CLOSING_RATIO),
|
||
0.0, 1.0))
|
||
approach_decel = VISION_UNTRACKED_SLOW_LEAD_MAX_DECEL * np.clip(
|
||
0.5 * time_factor + 0.3 * prob_factor + 0.2 * closing_factor, 0.0, 1.0)
|
||
if approach_decel < VISION_UNTRACKED_SLOW_LEAD_MIN_DECEL:
|
||
return None
|
||
|
||
return max(accel_min, -approach_decel)
|
||
|
||
def get_vision_slow_stopped_lead_cap(self, lead, v_ego, accel_min, t_follow):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < VISION_SLOW_LEAD_MIN_MODEL_PROB or float(lead.vLead) > VISION_SLOW_LEAD_MAX_SPEED:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(lead.aLeadK))
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
closing_speed = max(0.0, v_ego - lead.vLead)
|
||
projected_closing_speed = closing_speed + lead_brake * reaction_t
|
||
if projected_closing_speed < VISION_SLOW_LEAD_MIN_CLOSING_SPEED:
|
||
return None
|
||
|
||
stop_gap = float(max(STOP_DISTANCE + 1.0, 2.5 + 0.15 * max(float(lead.vLead), 0.0)))
|
||
delay_buffer = projected_closing_speed * reaction_t
|
||
available_gap = max(float(lead.dRel) - stop_gap - delay_buffer, 0.5)
|
||
projected_ttc = available_gap / max(projected_closing_speed, 0.1)
|
||
if projected_ttc > VISION_SLOW_LEAD_TRIGGER_TTC:
|
||
return None
|
||
|
||
time_factor = float(np.clip((VISION_SLOW_LEAD_TRIGGER_TTC - projected_ttc) /
|
||
(VISION_SLOW_LEAD_TRIGGER_TTC - VISION_SLOW_LEAD_FULL_TTC), 0.0, 1.0))
|
||
prob_factor = float(np.clip((lead_prob - VISION_SLOW_LEAD_MIN_MODEL_PROB) /
|
||
(VISION_LEAD_APPROACH_FULL_MODEL_PROB - VISION_SLOW_LEAD_MIN_MODEL_PROB), 0.0, 1.0))
|
||
speed_factor = float(np.clip((VISION_SLOW_LEAD_MAX_SPEED - max(float(lead.vLead), 0.0)) /
|
||
VISION_SLOW_LEAD_MAX_SPEED, 0.0, 1.0))
|
||
required_decel = (projected_closing_speed ** 2) / (2.0 * available_gap)
|
||
decel_scale = 0.45 + 0.35 * time_factor + 0.20 * speed_factor
|
||
approach_decel = min(VISION_SLOW_LEAD_MAX_DECEL, required_decel * decel_scale)
|
||
approach_decel *= 0.65 + 0.35 * prob_factor
|
||
if approach_decel < VISION_SLOW_LEAD_MIN_DECEL:
|
||
return None
|
||
|
||
return max(accel_min, -approach_decel)
|
||
|
||
def tracked_vision_lead_approach_needs_immediate_brake(self, lead, v_ego, approach_cap):
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
projected_closing_speed = max(0.0, v_ego - float(lead.vLead)) + lead_brake * reaction_t
|
||
bypass_distance = max(VISION_LEAD_APPROACH_CONFIRM_BYPASS_DISTANCE_MIN,
|
||
VISION_LEAD_APPROACH_CONFIRM_BYPASS_DISTANCE_TIME * float(v_ego))
|
||
return (
|
||
approach_cap <= -VISION_LEAD_APPROACH_CONFIRM_BYPASS_DECEL or
|
||
projected_closing_speed >= VISION_LEAD_APPROACH_CONFIRM_BYPASS_CLOSING_SPEED or
|
||
lead_brake >= VISION_LEAD_APPROACH_CONFIRM_BYPASS_LEAD_BRAKE or
|
||
float(lead.dRel) <= bypass_distance
|
||
)
|
||
|
||
def get_dynamic_t_follow(self, base_t_follow, lead, v_ego):
|
||
base_t_follow = float(base_t_follow)
|
||
target_t_follow = base_t_follow
|
||
|
||
if lead is not None and lead.status:
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if bool(getattr(lead, "radar", False)) else 0.0))
|
||
if bool(getattr(lead, "radar", False)) or lead_prob >= VISION_LEAD_APPROACH_MIN_MODEL_PROB:
|
||
lead_brake = max(0.0, -float(lead.aLeadK))
|
||
closing_speed = max(0.0, v_ego - lead.vLead)
|
||
if closing_speed >= LEAD_APPROACH_TFOLLOW_MIN_CLOSING_SPEED or lead_brake >= LEAD_APPROACH_TFOLLOW_MIN_LEAD_BRAKE:
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, base_t_follow))
|
||
approach_window = max(LEAD_APPROACH_TFOLLOW_WINDOW_MIN, LEAD_APPROACH_TFOLLOW_WINDOW_GAIN * float(v_ego))
|
||
if float(lead.dRel) <= desired_gap + approach_window:
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
projected_closing_speed = closing_speed + 0.5 * lead_brake * reaction_t
|
||
gap_to_follow = max(float(lead.dRel) - desired_gap, 0.0)
|
||
time_to_follow = gap_to_follow / max(projected_closing_speed, 0.1)
|
||
time_factor = float(np.clip((LEAD_APPROACH_TFOLLOW_TRIGGER_TIME - time_to_follow) /
|
||
(LEAD_APPROACH_TFOLLOW_TRIGGER_TIME - LEAD_APPROACH_TFOLLOW_FULL_TIME), 0.0, 1.0))
|
||
closing_factor = float(np.clip(closing_speed / LEAD_APPROACH_TFOLLOW_MAX_CLOSING_SPEED, 0.0, 1.0))
|
||
brake_factor = float(np.clip(lead_brake / LEAD_APPROACH_TFOLLOW_MAX_LEAD_BRAKE, 0.0, 1.0))
|
||
target_delta = LEAD_APPROACH_TFOLLOW_MAX_DELTA * np.clip(
|
||
0.55 * time_factor + 0.25 * closing_factor + 0.20 * brake_factor, 0.0, 1.0)
|
||
if not bool(getattr(lead, "radar", False)):
|
||
gap_deficit = max(desired_gap - float(lead.dRel), 0.0)
|
||
gap_buffer = max(VISION_LEAD_TFOLLOW_GAP_BUFFER_MIN,
|
||
VISION_LEAD_TFOLLOW_GAP_BUFFER_GAIN * float(v_ego))
|
||
gap_factor = float(np.clip(gap_deficit / gap_buffer, 0.0, 1.0))
|
||
slow_lead_factor = float(np.clip((VISION_LEAD_TFOLLOW_SLOW_LEAD_SPEED - float(lead.vLead)) /
|
||
VISION_LEAD_TFOLLOW_SLOW_LEAD_SPEED, 0.0, 1.0))
|
||
vision_extra = VISION_LEAD_TFOLLOW_MAX_EXTRA_DELTA * np.clip(
|
||
0.40 * time_factor + 0.30 * gap_factor + 0.20 * slow_lead_factor + 0.10 * closing_factor,
|
||
0.0, 1.0)
|
||
target_delta += vision_extra
|
||
target_t_follow = base_t_follow + float(target_delta)
|
||
|
||
if self.effective_t_follow is None:
|
||
self.effective_t_follow = base_t_follow
|
||
|
||
rate = LEAD_APPROACH_TFOLLOW_RATE_UP if target_t_follow > self.effective_t_follow else LEAD_APPROACH_TFOLLOW_RATE_DOWN
|
||
step = rate * self.dt
|
||
self.effective_t_follow = float(np.clip(target_t_follow, self.effective_t_follow - step, self.effective_t_follow + step))
|
||
self.effective_t_follow = max(base_t_follow, self.effective_t_follow)
|
||
return self.effective_t_follow
|
||
|
||
def get_vision_low_speed_stop_buffer_cap(self, lead, v_ego, accel_min):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None, False
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < VISION_LOW_SPEED_STOP_BUFFER_MIN_MODEL_PROB:
|
||
return None, False
|
||
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
relative_speed = float(v_ego) - lead_speed
|
||
closing_speed = max(0.0, v_ego - lead_speed)
|
||
entry_context = (
|
||
v_ego <= VISION_LOW_SPEED_STOP_BUFFER_MAX_EGO_SPEED and
|
||
lead_speed <= VISION_LOW_SPEED_STOP_BUFFER_MAX_LEAD_SPEED and
|
||
closing_speed >= VISION_LOW_SPEED_STOP_BUFFER_MIN_CLOSING_SPEED
|
||
)
|
||
hold_context = (
|
||
v_ego <= VISION_LOW_SPEED_STOP_BUFFER_MAX_EGO_SPEED and
|
||
lead_speed <= VISION_LOW_SPEED_STOP_BUFFER_HOLD_MAX_LEAD_SPEED and
|
||
relative_speed >= VISION_LOW_SPEED_STOP_BUFFER_MIN_HOLD_REL_SPEED
|
||
)
|
||
|
||
now_t = time.monotonic()
|
||
entry_buffer = max(3.2, VISION_LOW_SPEED_STOP_BUFFER_BASE +
|
||
VISION_LOW_SPEED_STOP_BUFFER_EGO_GAIN * float(v_ego) +
|
||
VISION_LOW_SPEED_STOP_BUFFER_LEAD_GAIN * lead_speed)
|
||
release_buffer = entry_buffer + VISION_LOW_SPEED_STOP_BUFFER_RELEASE_MARGIN
|
||
if entry_context and float(lead.dRel) <= entry_buffer:
|
||
self.vision_low_speed_stop_hold_until = now_t + VISION_LOW_SPEED_STOP_BUFFER_HOLD_TIME
|
||
|
||
latched = now_t < self.vision_low_speed_stop_hold_until
|
||
active = bool(
|
||
(entry_context and float(lead.dRel) <= entry_buffer) or
|
||
(latched and hold_context and float(lead.dRel) <= release_buffer)
|
||
)
|
||
if not active:
|
||
return None, False
|
||
|
||
min_stop_brake = VISION_LOW_SPEED_STOP_BUFFER_MIN_BRAKE + VISION_LOW_SPEED_STOP_BUFFER_BRAKE_GAIN * float(v_ego)
|
||
return max(accel_min, -min_stop_brake), True
|
||
|
||
def get_vision_close_stop_hold_cap(self, lead, v_ego, accel_min, should_stop):
|
||
if not should_stop or lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < VISION_CLOSE_STOP_HOLD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
near_standstill_settle = bool(
|
||
float(v_ego) <= VISION_CLOSE_SETTLE_MAX_EGO_SPEED and
|
||
lead_speed <= VISION_CLOSE_SETTLE_MAX_LEAD_SPEED and
|
||
float(lead.dRel) <= VISION_CLOSE_SETTLE_MAX_DISTANCE
|
||
)
|
||
max_lead_speed = VISION_CLOSE_SETTLE_MAX_LEAD_SPEED if near_standstill_settle else VISION_CLOSE_STOP_HOLD_MAX_LEAD_SPEED
|
||
max_distance = VISION_CLOSE_SETTLE_MAX_DISTANCE if near_standstill_settle else VISION_CLOSE_STOP_HOLD_MAX_DISTANCE
|
||
if (
|
||
float(v_ego) > VISION_CLOSE_STOP_HOLD_MAX_EGO_SPEED or
|
||
lead_speed > max_lead_speed or
|
||
float(lead.dRel) > max_distance
|
||
):
|
||
return None
|
||
|
||
distance_factor = float(np.clip((max_distance - float(lead.dRel)) /
|
||
max(max_distance - 1.8, 0.1), 0.0, 1.0))
|
||
speed_factor = float(np.clip(float(v_ego) / max(VISION_CLOSE_STOP_HOLD_MAX_EGO_SPEED, 0.1), 0.0, 1.0))
|
||
hold_brake = VISION_CLOSE_STOP_HOLD_MIN_BRAKE + 0.08 * distance_factor + 0.08 * speed_factor
|
||
if near_standstill_settle:
|
||
settle_brake = VISION_CLOSE_SETTLE_MIN_BRAKE + 0.10 * distance_factor + 0.04 * speed_factor
|
||
hold_brake = max(hold_brake, settle_brake)
|
||
hold_brake = float(np.clip(hold_brake, VISION_CLOSE_STOP_HOLD_MIN_BRAKE, VISION_CLOSE_STOP_HOLD_MAX_BRAKE))
|
||
brake_floor = -hold_brake
|
||
return brake_floor if accel_min >= 0.0 else max(accel_min, brake_floor)
|
||
|
||
def get_vision_close_release_hold_cap(self, lead, v_ego, accel_min, should_stop):
|
||
if should_stop or lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < VISION_CLOSE_RELEASE_HOLD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
lead_delta = lead_speed - float(v_ego)
|
||
near_standstill_settle = bool(
|
||
float(v_ego) <= VISION_CLOSE_SETTLE_MAX_EGO_SPEED and
|
||
lead_speed <= VISION_CLOSE_SETTLE_MAX_LEAD_SPEED and
|
||
float(lead.dRel) <= VISION_CLOSE_SETTLE_MAX_DISTANCE and
|
||
lead_delta <= VISION_CLOSE_SETTLE_MAX_LEAD_DELTA
|
||
)
|
||
max_lead_speed = VISION_CLOSE_SETTLE_MAX_LEAD_SPEED if near_standstill_settle else VISION_CLOSE_RELEASE_HOLD_MAX_LEAD_SPEED
|
||
max_distance = VISION_CLOSE_SETTLE_MAX_DISTANCE if near_standstill_settle else VISION_CLOSE_RELEASE_HOLD_MAX_DISTANCE
|
||
max_lead_delta = VISION_CLOSE_SETTLE_MAX_LEAD_DELTA if near_standstill_settle else VISION_CLOSE_RELEASE_HOLD_MAX_LEAD_DELTA
|
||
if (
|
||
float(v_ego) > VISION_CLOSE_RELEASE_HOLD_MAX_EGO_SPEED or
|
||
lead_speed > max_lead_speed or
|
||
float(lead.dRel) > max_distance or
|
||
lead_delta < VISION_CLOSE_RELEASE_HOLD_MIN_LEAD_DELTA or
|
||
lead_delta > max_lead_delta
|
||
):
|
||
return None
|
||
|
||
distance_factor = float(np.clip((max_distance - float(lead.dRel)) /
|
||
max(max_distance - 2.8, 0.1), 0.0, 1.0))
|
||
speed_factor = float(np.clip(float(v_ego) / max(VISION_CLOSE_RELEASE_HOLD_MAX_EGO_SPEED, 0.1), 0.0, 1.0))
|
||
delta_factor = float(np.clip((max_lead_delta - lead_delta) /
|
||
max(max_lead_delta - VISION_CLOSE_RELEASE_HOLD_MIN_LEAD_DELTA, 0.1),
|
||
0.0, 1.0))
|
||
hold_brake = VISION_CLOSE_RELEASE_HOLD_MIN_BRAKE + 0.12 * distance_factor + 0.06 * speed_factor + 0.04 * delta_factor
|
||
if near_standstill_settle:
|
||
settle_brake = VISION_CLOSE_SETTLE_MIN_BRAKE + 0.08 * distance_factor + 0.02 * delta_factor
|
||
hold_brake = max(hold_brake, settle_brake)
|
||
hold_brake = float(np.clip(hold_brake, VISION_CLOSE_RELEASE_HOLD_MIN_BRAKE, VISION_CLOSE_RELEASE_HOLD_MAX_BRAKE))
|
||
brake_floor = -hold_brake
|
||
return brake_floor if accel_min >= 0.0 else max(accel_min, brake_floor)
|
||
|
||
def get_vision_close_settle_cap(self, lead, v_ego, accel_min, stop_guard_active):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < VISION_CLOSE_STOP_HOLD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
lead_delta = lead_speed - float(v_ego)
|
||
if (
|
||
float(v_ego) > VISION_CLOSE_SETTLE_MAX_EGO_SPEED or
|
||
lead_speed > VISION_CLOSE_SETTLE_MAX_LEAD_SPEED or
|
||
float(lead.dRel) > VISION_CLOSE_SETTLE_MAX_DISTANCE or
|
||
lead_delta > VISION_CLOSE_SETTLE_MAX_LEAD_DELTA
|
||
):
|
||
return None
|
||
|
||
if not stop_guard_active and lead_delta < 0.0:
|
||
return None
|
||
|
||
distance_factor = float(np.clip((VISION_CLOSE_SETTLE_MAX_DISTANCE - float(lead.dRel)) /
|
||
max(VISION_CLOSE_SETTLE_MAX_DISTANCE - 2.8, 0.1), 0.0, 1.0))
|
||
delta_factor = float(np.clip((VISION_CLOSE_SETTLE_MAX_LEAD_DELTA - lead_delta) /
|
||
max(VISION_CLOSE_SETTLE_MAX_LEAD_DELTA, 0.1), 0.0, 1.0))
|
||
hold_brake = VISION_CLOSE_SETTLE_MIN_BRAKE + 0.10 * distance_factor + 0.04 * delta_factor
|
||
hold_brake = float(np.clip(hold_brake, VISION_CLOSE_SETTLE_MIN_BRAKE, VISION_CLOSE_SETTLE_MAX_BRAKE))
|
||
brake_floor = -hold_brake
|
||
return brake_floor if accel_min >= 0.0 else max(accel_min, brake_floor)
|
||
|
||
def get_vision_close_final_guard_cap(self, lead, v_ego, accel_min):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
if (
|
||
lead_prob < VISION_CLOSE_STOP_HOLD_MIN_MODEL_PROB or
|
||
float(v_ego) > VISION_CLOSE_FINAL_GUARD_MAX_EGO_SPEED or
|
||
lead_speed > VISION_CLOSE_FINAL_GUARD_MAX_LEAD_SPEED or
|
||
float(lead.dRel) > VISION_CLOSE_FINAL_GUARD_MAX_DISTANCE
|
||
):
|
||
return None
|
||
|
||
distance_factor = float(np.clip((VISION_CLOSE_FINAL_GUARD_MAX_DISTANCE - float(lead.dRel)) /
|
||
max(VISION_CLOSE_FINAL_GUARD_MAX_DISTANCE - 2.8, 0.1), 0.0, 1.0))
|
||
hold_brake = VISION_CLOSE_FINAL_GUARD_MIN_BRAKE + 0.10 * distance_factor
|
||
hold_brake = float(np.clip(hold_brake, VISION_CLOSE_FINAL_GUARD_MIN_BRAKE, VISION_CLOSE_FINAL_GUARD_MAX_BRAKE))
|
||
brake_floor = -hold_brake
|
||
return brake_floor if accel_min >= 0.0 else max(accel_min, brake_floor)
|
||
|
||
def _update_manual_stop_resume_override(self, sm):
|
||
now_t = time.monotonic()
|
||
lead = sm["radarState"].leadOne
|
||
no_lead = not bool(getattr(lead, "status", False))
|
||
try:
|
||
starpilot_car_state = sm["starpilotCarState"]
|
||
except KeyError:
|
||
starpilot_car_state = None
|
||
accel_pressed = bool(getattr(starpilot_car_state, "accelPressed", False))
|
||
model_should_stop = bool(getattr(sm["modelV2"].action, "shouldStop", False))
|
||
standstill = bool(getattr(sm["carState"], "standstill", False))
|
||
forcing_stop = bool(getattr(sm["starpilotPlan"], "forcingStop", False))
|
||
red_light = bool(getattr(sm["starpilotPlan"], "redLight", False))
|
||
|
||
if standstill and no_lead and accel_pressed and (forcing_stop or red_light or model_should_stop):
|
||
self.manual_stop_resume_override_until = now_t + MANUAL_STOP_RESUME_OVERRIDE_TIME
|
||
|
||
return bool(
|
||
no_lead and
|
||
float(getattr(sm["carState"], "vEgo", 0.0)) < MANUAL_STOP_RESUME_OVERRIDE_MAX_SPEED and
|
||
now_t < self.manual_stop_resume_override_until
|
||
)
|
||
|
||
def is_confident_lead_depart(self, lead, v_ego):
|
||
if lead is None or not lead.status:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < LEAD_DEPART_ACCEL_HOLD_MIN_MODEL_PROB:
|
||
return False
|
||
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
lead_delta = lead_speed - float(v_ego)
|
||
lead_accel = float(getattr(lead, "aLeadK", 0.0))
|
||
return bool(
|
||
float(lead.dRel) >= LEAD_DEPART_CONFIDENT_MIN_GAP and
|
||
float(lead.dRel) <= LEAD_DEPART_CONFIDENT_MAX_GAP and
|
||
lead_speed >= LEAD_DEPART_CONFIDENT_MIN_LEAD_SPEED and
|
||
lead_delta >= LEAD_DEPART_CONFIDENT_MIN_LEAD_DELTA and
|
||
lead_accel >= LEAD_DEPART_CONFIDENT_MIN_LEAD_ACCEL
|
||
)
|
||
|
||
@staticmethod
|
||
def get_centered_model_lead(model_data):
|
||
try:
|
||
leads = model_data.leadsV3
|
||
except Exception:
|
||
return None
|
||
|
||
best_candidate = None
|
||
for i in range(3):
|
||
try:
|
||
lead = leads[i]
|
||
prob = float(lead.prob)
|
||
x = float(lead.x[0])
|
||
y = float(lead.y[0])
|
||
v = float(lead.v[0])
|
||
except Exception:
|
||
continue
|
||
|
||
if (
|
||
prob < RADAR_DEPART_CONFLICT_MIN_MODEL_PROB or
|
||
x <= 0.0 or
|
||
x > RADAR_DEPART_CONFLICT_MAX_MODEL_DISTANCE or
|
||
abs(y) > RADAR_DEPART_CONFLICT_MAX_MODEL_LATERAL or
|
||
max(v, 0.0) > RADAR_DEPART_CONFLICT_MAX_MODEL_LEAD_SPEED
|
||
):
|
||
continue
|
||
|
||
if best_candidate is None or x < best_candidate[0]:
|
||
best_candidate = (x, y, v, prob)
|
||
|
||
return best_candidate
|
||
|
||
def has_offcenter_radar_depart_conflict(self, sm):
|
||
if float(getattr(sm["carState"], "vEgo", 0.0)) > RADAR_DEPART_CONFLICT_MAX_EGO_SPEED:
|
||
return False
|
||
|
||
centered_model_lead = self.get_centered_model_lead(sm["modelV2"])
|
||
if centered_model_lead is None:
|
||
return False
|
||
|
||
centered_model_dist = float(centered_model_lead[0])
|
||
for lead in (self.lead_one, self.lead_two):
|
||
if not lead.status or not bool(getattr(lead, "radar", False)):
|
||
continue
|
||
|
||
lead_dist = float(getattr(lead, "dRel", 0.0))
|
||
if lead_dist <= 0.0 or lead_dist > RADAR_DEPART_CONFLICT_MAX_RADAR_DISTANCE:
|
||
continue
|
||
if abs(float(getattr(lead, "yRel", 0.0))) < RADAR_DEPART_CONFLICT_MIN_RADAR_LATERAL:
|
||
continue
|
||
if abs(lead_dist - centered_model_dist) > RADAR_DEPART_CONFLICT_MAX_DISTANCE_MISMATCH:
|
||
continue
|
||
|
||
return True
|
||
|
||
return False
|
||
|
||
def get_lead_depart_accel_floor(self, lead, v_ego, model_desired_accel):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < LEAD_DEPART_ACCEL_HOLD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_speed = max(float(lead.vLead), 0.0)
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
lead_delta = lead_speed - float(v_ego)
|
||
confident_depart = self.is_confident_lead_depart(lead, v_ego)
|
||
min_lead_speed = LEAD_DEPART_CONFIDENT_MIN_LEAD_SPEED if confident_depart else LEAD_DEPART_ACCEL_HOLD_MIN_LEAD_SPEED
|
||
min_lead_delta = LEAD_DEPART_CONFIDENT_MIN_LEAD_DELTA if confident_depart else LEAD_DEPART_ACCEL_HOLD_MIN_LEAD_DELTA
|
||
min_gap = LEAD_DEPART_CONFIDENT_MIN_GAP if confident_depart else LEAD_DEPART_ACCEL_HOLD_MIN_GAP
|
||
min_model_accel = 0.0 if confident_depart else LEAD_DEPART_ACCEL_HOLD_MIN_MODEL_ACCEL
|
||
if (
|
||
float(v_ego) > LEAD_DEPART_ACCEL_HOLD_MAX_EGO_SPEED or
|
||
lead_speed < min_lead_speed or
|
||
lead_delta < min_lead_delta or
|
||
float(lead.dRel) < min_gap or
|
||
lead_brake > LEAD_DEPART_ACCEL_HOLD_MAX_LEAD_BRAKE or
|
||
float(model_desired_accel) < min_model_accel
|
||
):
|
||
return None
|
||
|
||
gap_factor = float(np.clip((float(lead.dRel) - LEAD_DEPART_ACCEL_HOLD_MIN_GAP) /
|
||
max(LEAD_DEPART_ACCEL_HOLD_FULL_GAP - LEAD_DEPART_ACCEL_HOLD_MIN_GAP, 0.1), 0.0, 1.0))
|
||
lead_factor = float(np.clip((lead_speed - LEAD_DEPART_ACCEL_HOLD_MIN_LEAD_SPEED) /
|
||
max(LEAD_DEPART_ACCEL_HOLD_FULL_LEAD_SPEED - LEAD_DEPART_ACCEL_HOLD_MIN_LEAD_SPEED, 0.1), 0.0, 1.0))
|
||
accel_cap = LEAD_DEPART_ACCEL_HOLD_MIN_ACCEL + (LEAD_DEPART_ACCEL_HOLD_MAX_ACCEL - LEAD_DEPART_ACCEL_HOLD_MIN_ACCEL) * np.clip(
|
||
0.55 * lead_factor + 0.45 * gap_factor, 0.0, 1.0)
|
||
return min(accel_cap, max(float(model_desired_accel), LEAD_DEPART_ACCEL_HOLD_MIN_ACCEL))
|
||
|
||
def get_low_speed_weak_lead_accel_cap(self, lead, v_ego):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
d_rel = float(getattr(lead, "dRel", 0.0))
|
||
lead_speed = max(float(getattr(lead, "vLead", 0.0)), 0.0)
|
||
lead_delta = lead_speed - float(v_ego)
|
||
lead_accel = float(getattr(lead, "aLeadK", 0.0))
|
||
lead_lateral = abs(float(getattr(lead, "yRel", 0.0)))
|
||
if (
|
||
float(v_ego) > LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_EGO_SPEED or
|
||
d_rel > LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_DISTANCE or
|
||
lead_speed > LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_SPEED or
|
||
lead_lateral > LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET or
|
||
lead_delta > LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_DELTA or
|
||
lead_accel > LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_ACCEL
|
||
):
|
||
return None
|
||
|
||
if (
|
||
float(v_ego) <= LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MAX_EGO_SPEED and
|
||
d_rel >= LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_GAP and
|
||
lead_speed >= LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_LEAD_SPEED and
|
||
lead_delta >= LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_LEAD_DELTA and
|
||
lead_accel >= LOW_SPEED_WEAK_LEAD_ACCEL_CAP_STRONG_DEPART_MIN_LEAD_ACCEL
|
||
):
|
||
return None
|
||
|
||
distance_factor = float(np.clip(
|
||
(d_rel - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_DISTANCE) /
|
||
max(LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_DISTANCE - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_DISTANCE, 0.1),
|
||
0.0, 1.0,
|
||
))
|
||
delta_factor = float(np.clip(
|
||
(lead_delta - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_LEAD_DELTA) /
|
||
max(LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_DELTA - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_LEAD_DELTA, 0.1),
|
||
0.0, 1.0,
|
||
))
|
||
accel_factor = float(np.clip(
|
||
(lead_accel - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_LEAD_ACCEL) /
|
||
max(LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_LEAD_ACCEL - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_LEAD_ACCEL, 0.1),
|
||
0.0, 1.0,
|
||
))
|
||
cap_strength = float(np.clip(0.5 * distance_factor + 0.3 * delta_factor + 0.2 * accel_factor, 0.0, 1.0))
|
||
return LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_ACCEL + (
|
||
LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MAX_ACCEL - LOW_SPEED_WEAK_LEAD_ACCEL_CAP_MIN_ACCEL
|
||
) * cap_strength
|
||
|
||
def get_standstill_stopped_lead_guard_cap(self, lead, v_ego, accel_min, stop_distance,
|
||
release_ready, confident_depart_ready):
|
||
if lead is None or not lead.status or release_ready or confident_depart_ready:
|
||
return None
|
||
if float(v_ego) > STANDSTILL_STOPPED_LEAD_GUARD_MAX_EGO_SPEED:
|
||
return None
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < STANDSTILL_STOPPED_LEAD_GUARD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > STANDSTILL_STOPPED_LEAD_GUARD_MAX_LATERAL_OFFSET:
|
||
return None
|
||
|
||
lead_speed = max(float(getattr(lead, "vLead", 0.0)), 0.0)
|
||
lead_delta = lead_speed - float(v_ego)
|
||
max_distance = max(
|
||
STANDSTILL_STOPPED_LEAD_GUARD_MIN_DISTANCE,
|
||
float(stop_distance) + STANDSTILL_STOPPED_LEAD_GUARD_DISTANCE_MARGIN,
|
||
)
|
||
if (
|
||
float(getattr(lead, "dRel", float("inf"))) > max_distance or
|
||
lead_speed > STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_SPEED or
|
||
lead_delta > STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_DELTA
|
||
):
|
||
return None
|
||
|
||
distance_factor = float(np.clip((max_distance - float(lead.dRel)) /
|
||
max(max_distance - STANDSTILL_STOPPED_LEAD_GUARD_MIN_DISTANCE, 0.1),
|
||
0.0, 1.0))
|
||
speed_factor = float(np.clip(lead_speed / max(STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_SPEED, 0.1), 0.0, 1.0))
|
||
delta_factor = float(np.clip((STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_DELTA - lead_delta) /
|
||
max(STANDSTILL_STOPPED_LEAD_GUARD_MAX_LEAD_DELTA, 0.1),
|
||
0.0, 1.0))
|
||
hold_brake = STANDSTILL_STOPPED_LEAD_GUARD_MIN_BRAKE + 0.06 * distance_factor + 0.02 * speed_factor + 0.02 * delta_factor
|
||
hold_brake = float(np.clip(
|
||
hold_brake,
|
||
STANDSTILL_STOPPED_LEAD_GUARD_MIN_BRAKE,
|
||
STANDSTILL_STOPPED_LEAD_GUARD_MAX_BRAKE,
|
||
))
|
||
brake_floor = -hold_brake
|
||
return brake_floor if accel_min >= 0.0 else max(accel_min, brake_floor)
|
||
|
||
def is_stable_post_departure_pullaway(self, lead, v_ego, t_follow):
|
||
if lead is None or not lead.status or float(v_ego) < POST_DEPARTURE_FOLLOW_BYPASS_MIN_SPEED:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < POST_DEPARTURE_FOLLOW_BYPASS_MIN_MODEL_PROB:
|
||
return False
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET:
|
||
return False
|
||
|
||
lead_delta = float(lead.vLead) - float(v_ego)
|
||
lead_accel = float(getattr(lead, "aLeadK", 0.0))
|
||
if (lead_delta < POST_DEPARTURE_FOLLOW_BYPASS_MIN_LEAD_DELTA or
|
||
lead_accel < POST_DEPARTURE_FOLLOW_BYPASS_MIN_LEAD_ACCEL):
|
||
return False
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(t_follow)
|
||
return headway_margin >= POST_DEPARTURE_FOLLOW_BYPASS_MIN_HEADWAY_MARGIN
|
||
|
||
def is_comfortable_accelerating_away_follow(self, lead, v_ego, t_follow):
|
||
if lead is None or not lead.status or float(v_ego) < POST_DEPARTURE_FOLLOW_BYPASS_MIN_SPEED:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < COMFORTABLE_PULLAWAY_FOLLOW_MIN_MODEL_PROB:
|
||
return False
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET:
|
||
return False
|
||
|
||
lead_delta = float(lead.vLead) - float(v_ego)
|
||
lead_accel = float(getattr(lead, "aLeadK", 0.0))
|
||
if (lead_delta < COMFORTABLE_PULLAWAY_FOLLOW_MIN_LEAD_DELTA or
|
||
lead_accel < COMFORTABLE_PULLAWAY_FOLLOW_MIN_LEAD_ACCEL):
|
||
return False
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(t_follow)
|
||
return headway_margin >= COMFORTABLE_PULLAWAY_FOLLOW_MIN_HEADWAY_MARGIN
|
||
|
||
def post_departure_follow_settle_active(self, lead, v_ego, t_follow):
|
||
if lead is None or not lead.status:
|
||
return False
|
||
if time.monotonic() > self.post_departure_follow_settle_until:
|
||
self.post_departure_follow_settle_until = 0.0
|
||
return False
|
||
if float(v_ego) < POST_DEPARTURE_FOLLOW_SETTLE_MIN_SPEED:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < POST_DEPARTURE_FOLLOW_SETTLE_MIN_MODEL_PROB:
|
||
return False
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > POST_DEPARTURE_FOLLOW_SETTLE_MAX_LATERAL_OFFSET:
|
||
return False
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(t_follow)
|
||
if headway_margin <= POST_DEPARTURE_FOLLOW_SETTLE_COMPLETE_HEADWAY_MARGIN:
|
||
self.post_departure_follow_settle_until = 0.0
|
||
return False
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > POST_DEPARTURE_FOLLOW_SETTLE_MAX_LEAD_BRAKE:
|
||
self.post_departure_follow_settle_until = 0.0
|
||
return False
|
||
|
||
closing_speed = max(float(v_ego) - float(lead.vLead), 0.0)
|
||
if closing_speed > POST_DEPARTURE_FOLLOW_SETTLE_MAX_CLOSING_SPEED:
|
||
self.post_departure_follow_settle_until = 0.0
|
||
return False
|
||
|
||
return headway_margin >= POST_DEPARTURE_FOLLOW_SETTLE_MIN_HEADWAY_MARGIN
|
||
|
||
def is_spacious_low_closure_follow(self, lead, v_ego, t_follow):
|
||
if lead is None or not lead.status or float(v_ego) < CRUISE_TRACKED_LEAD_ACCEL_CAP_MIN_SPEED:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < SPACIOUS_TRACKED_FOLLOW_MIN_MODEL_PROB:
|
||
return False
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET:
|
||
return False
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > SPACIOUS_TRACKED_FOLLOW_MAX_LEAD_BRAKE:
|
||
return False
|
||
|
||
closing_speed = max(float(v_ego) - float(lead.vLead), 0.0)
|
||
if closing_speed > SPACIOUS_TRACKED_FOLLOW_MAX_CLOSING_SPEED:
|
||
return False
|
||
|
||
if self.raw_close_lead_needs_control(lead, v_ego):
|
||
return False
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(t_follow)
|
||
return headway_margin >= SPACIOUS_TRACKED_FOLLOW_MIN_HEADWAY_MARGIN
|
||
|
||
def spacious_follow_cap_bypass_active(self, lead, v_ego, t_follow, tracking_lead_active):
|
||
if not tracking_lead_active or time.monotonic() > self.spacious_follow_cap_bypass_until:
|
||
return False
|
||
return self.is_spacious_low_closure_follow(lead, v_ego, t_follow)
|
||
|
||
def get_lead_catchup_accel_cap(self, lead, v_ego, t_follow, current_source=None, tracking_lead_active=False):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
|
||
if self.post_departure_follow_settle_active(lead, v_ego, t_follow):
|
||
return None
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
low_speed_follow_window = (
|
||
not lead_radar and
|
||
v_ego <= LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_SPEED and
|
||
lead_prob >= LOW_SPEED_FOLLOW_ACCEL_CAP_MIN_MODEL_PROB and
|
||
lead_brake <= LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_LEAD_BRAKE
|
||
)
|
||
if v_ego < LEAD_CATCHUP_ACCEL_MIN_EGO and not low_speed_follow_window:
|
||
return None
|
||
|
||
lead_delta = float(lead.vLead) - float(v_ego)
|
||
min_lead_delta = LOW_SPEED_FOLLOW_ACCEL_CAP_MIN_LEAD_DELTA if low_speed_follow_window else LEAD_CATCHUP_ACCEL_MIN_LEAD_DELTA
|
||
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
|
||
if low_speed_follow_window:
|
||
gap_buffer = max(LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_GAP_BUFFER_MIN,
|
||
LOW_SPEED_FOLLOW_ACCEL_CAP_MAX_GAP_BUFFER_GAIN * float(v_ego))
|
||
else:
|
||
gap_buffer = max(LEAD_CATCHUP_ACCEL_MAX_GAP_BUFFER_MIN,
|
||
LEAD_CATCHUP_ACCEL_MAX_GAP_BUFFER_GAIN * float(v_ego))
|
||
gap_error = float(lead.dRel) - desired_gap
|
||
|
||
radar_matched_follow_active = (
|
||
lead_radar and
|
||
tracking_lead_active and
|
||
self.lead_is_matched_follow_window(lead, v_ego, t_follow)
|
||
)
|
||
if radar_matched_follow_active and gap_error > (gap_buffer - RADAR_MATCHED_FOLLOW_CATCHUP_CAP_BUFFER_MARGIN):
|
||
return None
|
||
|
||
if (radar_matched_follow_active and current_source == "cruise" and
|
||
gap_error <= RADAR_MATCHED_FOLLOW_CATCHUP_HOLD_MAX_GAP_ERROR and
|
||
lead_delta < min_lead_delta):
|
||
return RADAR_MATCHED_FOLLOW_CATCHUP_HOLD_CAP
|
||
|
||
if lead_delta < min_lead_delta:
|
||
return None
|
||
|
||
if gap_error > gap_buffer:
|
||
return None
|
||
|
||
if tracking_lead_active and self.is_comfortable_accelerating_away_follow(lead, v_ego, t_follow):
|
||
return None
|
||
if current_source == "cruise" and self.spacious_follow_cap_bypass_active(lead, v_ego, t_follow, tracking_lead_active):
|
||
return None
|
||
|
||
if not low_speed_follow_window and self.is_stable_post_departure_pullaway(lead, v_ego, t_follow):
|
||
return None
|
||
|
||
# If the lead is already pace-matched or pulling away, keep any catch-up
|
||
# accel very small while we're near the follow target so we don't surge into
|
||
# the lead and immediately ask for brake again.
|
||
if low_speed_follow_window:
|
||
edge_cap = float(np.interp(lead_delta, [min_lead_delta, 0.0, 1.0, 2.0], [0.20, 0.24, 0.38, 0.55]))
|
||
near_cap = min(edge_cap, 0.16)
|
||
else:
|
||
edge_cap = float(np.interp(lead_delta, [-0.5, 0.0, 1.0], [0.16, 0.08, 0.02]))
|
||
near_cap = min(edge_cap, 0.03)
|
||
gap_factor = float(np.clip(max(gap_error, 0.0) / max(gap_buffer, 0.1), 0.0, 1.0))
|
||
return float(np.interp(gap_factor, [0.0, 1.0], [near_cap, edge_cap]))
|
||
|
||
def get_low_speed_follow_transition_brake_cap(self, lead, v_ego, t_follow, prev_output_a_target, output_a_target):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
if bool(getattr(lead, "radar", False)):
|
||
return None
|
||
if not (LOW_SPEED_FOLLOW_TRANSITION_MIN_SPEED <= float(v_ego) <= LOW_SPEED_FOLLOW_TRANSITION_MAX_SPEED):
|
||
return None
|
||
if prev_output_a_target <= LOW_SPEED_FOLLOW_TRANSITION_PREV_ACCEL_MIN:
|
||
return None
|
||
if output_a_target >= LOW_SPEED_FOLLOW_TRANSITION_TARGET_BRAKE_MIN:
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < LOW_SPEED_FOLLOW_TRANSITION_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > LOW_SPEED_FOLLOW_TRANSITION_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
closing_speed = max(0.0, float(v_ego) - float(lead.vLead))
|
||
if closing_speed > LOW_SPEED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED:
|
||
return None
|
||
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
|
||
if float(lead.dRel) < desired_gap + LOW_SPEED_FOLLOW_TRANSITION_MIN_GAP_MARGIN:
|
||
return None
|
||
|
||
cap_decel = float(np.interp(
|
||
closing_speed,
|
||
[0.0, LOW_SPEED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED],
|
||
[LOW_SPEED_FOLLOW_TRANSITION_MIN_BRAKE, LOW_SPEED_FOLLOW_TRANSITION_MAX_BRAKE],
|
||
))
|
||
return -cap_decel
|
||
|
||
def get_cruise_tracking_lead_accel_cap(self, lead, v_ego, t_follow, current_source, tracking_lead_active):
|
||
if lead is None or not lead.status or current_source != "cruise":
|
||
return None
|
||
if self.post_departure_follow_settle_active(lead, v_ego, t_follow):
|
||
return None
|
||
if not (CRUISE_TRACKED_LEAD_ACCEL_CAP_MIN_SPEED <= float(v_ego) <= CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_SPEED):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if bool(getattr(lead, "radar", False)) else 0.0))
|
||
if not bool(getattr(lead, "radar", False)) and lead_prob < CRUISE_TRACKED_LEAD_ACCEL_CAP_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LEAD_BRAKE and not tracking_lead_active:
|
||
return None
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_LATERAL_OFFSET:
|
||
return None
|
||
|
||
lead_delta = float(lead.vLead) - float(v_ego)
|
||
if lead_delta > CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_PULLAWAY_SPEED:
|
||
return None
|
||
|
||
if tracking_lead_active and self.is_comfortable_accelerating_away_follow(lead, v_ego, t_follow):
|
||
return None
|
||
if self.spacious_follow_cap_bypass_active(lead, v_ego, t_follow, tracking_lead_active):
|
||
return None
|
||
|
||
closing_speed = max(float(v_ego) - float(lead.vLead), 0.0)
|
||
raw_close_lead = self.raw_close_lead_needs_control(lead, v_ego)
|
||
unresolved_slow_lead = (
|
||
closing_speed >= CRUISE_TRACKED_LEAD_ACCEL_CAP_UNRESOLVED_MIN_CLOSING_SPEED and
|
||
lead_delta <= CRUISE_TRACKED_LEAD_ACCEL_CAP_UNRESOLVED_MAX_LEAD_DELTA
|
||
)
|
||
if not tracking_lead_active and not raw_close_lead and not unresolved_slow_lead:
|
||
return None
|
||
|
||
# Don't let a spacious, nearly pace-matched tracked lead toggle this cap on
|
||
# and off while cruise remains the source. That creates the square-wave
|
||
# accel "surge / give up / surge" behavior seen in real logs.
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(t_follow)
|
||
tracking_only_follow = tracking_lead_active and not raw_close_lead and not unresolved_slow_lead
|
||
if (tracking_only_follow and
|
||
headway_margin > CRUISE_TRACKED_LEAD_ACCEL_CAP_TRACKING_ONLY_MAX_HEADWAY_ABOVE_TARGET and
|
||
closing_speed < CRUISE_TRACKED_LEAD_ACCEL_CAP_TRACKING_ONLY_MAX_CLOSING_SPEED and
|
||
lead_brake <= CRUISE_TRACKED_LEAD_ACCEL_CAP_TRACKING_ONLY_MAX_LEAD_BRAKE):
|
||
return None
|
||
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
|
||
gap_error = float(lead.dRel) - desired_gap
|
||
gap_buffer = max(CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_GAP_BUFFER_MIN,
|
||
CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_GAP_BUFFER_GAIN * float(v_ego))
|
||
if gap_error > gap_buffer:
|
||
return None
|
||
|
||
# If the same lead is already accelerating away and we're no longer tight to
|
||
# the follow target, don't slam the accel cap back on just because lead_delta
|
||
# momentarily falls near the pull-away threshold. That produces the repeated
|
||
# 0.18 m/s^2 "surge / give up / surge" behavior seen in real logs.
|
||
lead_accel = float(getattr(lead, "aLeadK", 0.0))
|
||
if self.is_stable_post_departure_pullaway(lead, v_ego, t_follow) or (
|
||
lead_delta >= CRUISE_TRACKED_LEAD_ACCEL_CAP_ACCEL_AWAY_MIN_LEAD_DELTA and
|
||
lead_accel >= CRUISE_TRACKED_LEAD_ACCEL_CAP_ACCEL_AWAY_MIN and
|
||
gap_error >= CRUISE_TRACKED_LEAD_ACCEL_CAP_ACCEL_AWAY_MIN_GAP_MARGIN
|
||
):
|
||
return None
|
||
|
||
base_cap = float(np.interp(
|
||
lead_delta,
|
||
[-1.5, -0.5, 0.0, 0.5, CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_PULLAWAY_SPEED],
|
||
[0.0, 0.04, 0.08, 0.12, 0.16],
|
||
))
|
||
|
||
if raw_close_lead:
|
||
base_cap = min(base_cap, float(np.interp(closing_speed, [0.5, 1.5, 3.5], [0.10, 0.05, 0.0])))
|
||
else:
|
||
base_cap = min(base_cap, float(np.interp(closing_speed, [0.0, 1.0, 2.0], [0.18, 0.12, 0.06])))
|
||
|
||
if gap_error <= 0.0:
|
||
return max(0.0, base_cap)
|
||
|
||
gap_factor = float(np.clip(gap_error / max(gap_buffer, 0.1), 0.0, 1.0))
|
||
cap = min(CRUISE_TRACKED_LEAD_ACCEL_CAP_MAX_ACCEL, base_cap + 0.06 * gap_factor)
|
||
return max(0.0, cap)
|
||
|
||
def get_cruise_tracking_lead_accel_transition_target(self, lead, v_ego, t_follow,
|
||
prev_output_a_target, output_a_target,
|
||
current_source):
|
||
if lead is None or not lead.status or current_source != "cruise":
|
||
return None
|
||
if not (CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_SPEED <= float(v_ego) <= CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_SPEED):
|
||
return None
|
||
|
||
target_delta = float(output_a_target) - float(prev_output_a_target)
|
||
if target_delta < CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_DELTA_A:
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if bool(getattr(lead, "radar", False)) else 0.0))
|
||
if not bool(getattr(lead, "radar", False)) and lead_prob < CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
if abs(float(getattr(lead, "yRel", 0.0))) > CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_LATERAL_OFFSET:
|
||
return None
|
||
|
||
lead_delta = float(lead.vLead) - float(v_ego)
|
||
if lead_delta > CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_PULLAWAY_SPEED:
|
||
return None
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(t_follow)
|
||
if headway_margin > CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_HEADWAY_ABOVE_TARGET:
|
||
return None
|
||
|
||
positive_step = float(np.interp(
|
||
lead_delta,
|
||
[-1.0, 0.0, 1.0, CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_PULLAWAY_SPEED],
|
||
[CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MIN_STEP,
|
||
0.08,
|
||
0.12,
|
||
CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_STEP],
|
||
))
|
||
if headway_margin > 0.0:
|
||
headway_factor = float(np.clip(
|
||
headway_margin / max(CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_HEADWAY_ABOVE_TARGET, 1e-3),
|
||
0.0,
|
||
1.0,
|
||
))
|
||
positive_step = float(np.interp(
|
||
headway_factor,
|
||
[0.0, 1.0],
|
||
[positive_step, CRUISE_TRACKED_LEAD_ACCEL_TRANSITION_MAX_STEP],
|
||
))
|
||
|
||
upper = float(prev_output_a_target) + positive_step
|
||
smoothed_target = float(min(output_a_target, upper))
|
||
return smoothed_target if smoothed_target < float(output_a_target) - 1e-6 else None
|
||
|
||
def lead_is_matched_follow_window(self, lead, v_ego, base_t_follow):
|
||
if lead is None or not lead.status or v_ego < STEADY_FOLLOW_SMOOTHING_MIN_SPEED:
|
||
return False
|
||
|
||
relative_speed = float(v_ego) - float(lead.vLead)
|
||
if not (STEADY_FOLLOW_BRAKE_CAP_MIN_REL_SPEED <= relative_speed <= STEADY_FOLLOW_SMOOTHING_MAX_CLOSING_SPEED):
|
||
return False
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > STEADY_FOLLOW_SMOOTHING_MAX_LEAD_BRAKE:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and not is_radarless_matched_follow_window(
|
||
v_ego,
|
||
lead.dRel,
|
||
lead.vLead,
|
||
base_t_follow,
|
||
radar=lead_radar,
|
||
lead_brake=lead_brake,
|
||
lead_prob=lead_prob,
|
||
):
|
||
return False
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
if actual_headway < max(STEADY_FOLLOW_BRAKE_CAP_MIN_HEADWAY, float(base_t_follow) - STEADY_FOLLOW_SMOOTHING_HEADWAY_BELOW_TARGET):
|
||
return False
|
||
if actual_headway > float(base_t_follow) + STEADY_FOLLOW_BRAKE_CAP_MAX_HEADWAY_ABOVE_TARGET:
|
||
return False
|
||
return True
|
||
|
||
def get_matched_follow_control_lead(self, v_ego, t_follow):
|
||
if self.mpc.source == 'lead1' and self.lead_is_matched_follow_window(self.lead_two, v_ego, t_follow):
|
||
return self.lead_two
|
||
if self.lead_is_matched_follow_window(self.lead_one, v_ego, t_follow):
|
||
return self.lead_one
|
||
if self.lead_is_matched_follow_window(self.lead_two, v_ego, t_follow):
|
||
return self.lead_two
|
||
return None
|
||
|
||
def get_follow_control_lead(self, lead_control_active, v_ego, t_follow, *, allow_optional_far_lead_logic=True):
|
||
if allow_optional_far_lead_logic:
|
||
matched_follow_lead = self.get_matched_follow_control_lead(v_ego, t_follow)
|
||
if matched_follow_lead is not None:
|
||
return matched_follow_lead
|
||
|
||
if not lead_control_active:
|
||
return None
|
||
|
||
if self.lead_one.status:
|
||
return self.lead_one
|
||
if self.lead_two.status:
|
||
return self.lead_two
|
||
return None
|
||
|
||
def lead_is_spacious_brake_cap_window(self, lead, v_ego, base_t_follow):
|
||
if lead is None or not lead.status or v_ego < STEADY_FOLLOW_SMOOTHING_MIN_SPEED:
|
||
return False
|
||
|
||
relative_speed = float(v_ego) - float(lead.vLead)
|
||
if not (STEADY_FOLLOW_BRAKE_CAP_MIN_REL_SPEED <= relative_speed <= STEADY_FOLLOW_BRAKE_CAP_MAX_CLOSING_SPEED):
|
||
return False
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > STEADY_FOLLOW_BRAKE_CAP_SPACIOUS_MAX_LEAD_BRAKE:
|
||
return False
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < STEADY_FOLLOW_SMOOTHING_MIN_MODEL_PROB:
|
||
return False
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
if actual_headway < max(float(base_t_follow) + STEADY_FOLLOW_BRAKE_CAP_SPACIOUS_HEADWAY_MARGIN,
|
||
STEADY_FOLLOW_BRAKE_CAP_SPACIOUS_MIN_HEADWAY):
|
||
return False
|
||
if actual_headway > float(base_t_follow) + STEADY_FOLLOW_BRAKE_CAP_MAX_HEADWAY_ABOVE_TARGET:
|
||
return False
|
||
return True
|
||
|
||
def get_matched_follow_brake_cap(self, lead, v_ego, base_t_follow):
|
||
if not (
|
||
self.lead_is_matched_follow_window(lead, v_ego, base_t_follow) or
|
||
self.lead_is_spacious_brake_cap_window(lead, v_ego, base_t_follow)
|
||
):
|
||
return None
|
||
|
||
relative_speed = float(v_ego) - float(lead.vLead)
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
cap_decel = float(np.interp(
|
||
relative_speed,
|
||
[STEADY_FOLLOW_BRAKE_CAP_MIN_REL_SPEED, 0.0, STEADY_FOLLOW_BRAKE_CAP_MAX_CLOSING_SPEED],
|
||
[STEADY_FOLLOW_BRAKE_CAP_OPENING_DECEL,
|
||
STEADY_FOLLOW_BRAKE_CAP_ZERO_REL_SPEED_DECEL,
|
||
STEADY_FOLLOW_BRAKE_CAP_MAX_DECEL],
|
||
))
|
||
headway_deficit = float(np.clip((float(base_t_follow) - actual_headway) / STEADY_FOLLOW_SMOOTHING_HEADWAY_BELOW_TARGET, 0.0, 1.0))
|
||
cap_decel = min(STEADY_FOLLOW_BRAKE_CAP_MAX_DECEL, cap_decel + 0.05 * headway_deficit)
|
||
return -cap_decel
|
||
|
||
def get_far_lead_brake_cap(self, lead, v_ego, base_t_follow):
|
||
if lead is None or not lead.status or v_ego < STEADY_FOLLOW_SMOOTHING_MIN_SPEED:
|
||
return None
|
||
|
||
if bool(getattr(lead, "radar", False)):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < FAR_LEAD_COMFORT_BRAKE_CAP_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
relative_speed = float(v_ego) - float(lead.vLead)
|
||
if not (FAR_LEAD_COMFORT_BRAKE_CAP_MIN_CLOSING_SPEED <= relative_speed <= FAR_LEAD_COMFORT_BRAKE_CAP_MAX_CLOSING_SPEED):
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > FAR_LEAD_COMFORT_BRAKE_CAP_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(base_t_follow)
|
||
if float(lead.dRel) < FAR_LEAD_COMFORT_BRAKE_CAP_MIN_DISTANCE or headway_margin < FAR_LEAD_COMFORT_BRAKE_CAP_MIN_HEADWAY_MARGIN:
|
||
return None
|
||
|
||
ttc = float(lead.dRel) / max(relative_speed, 1e-3)
|
||
if ttc < FAR_LEAD_COMFORT_BRAKE_CAP_MIN_TTC:
|
||
return None
|
||
|
||
cap_decel = float(np.interp(
|
||
relative_speed,
|
||
[FAR_LEAD_COMFORT_BRAKE_CAP_MIN_CLOSING_SPEED, FAR_LEAD_COMFORT_BRAKE_CAP_MAX_CLOSING_SPEED],
|
||
[FAR_LEAD_COMFORT_BRAKE_CAP_MIN_DECEL, FAR_LEAD_COMFORT_BRAKE_CAP_MAX_DECEL],
|
||
))
|
||
relax_decel = float(np.interp(
|
||
headway_margin,
|
||
[FAR_LEAD_COMFORT_BRAKE_CAP_MIN_HEADWAY_MARGIN, FAR_LEAD_COMFORT_BRAKE_CAP_FULL_HEADWAY_MARGIN],
|
||
[0.0, FAR_LEAD_COMFORT_BRAKE_CAP_FULL_RELAX_DECEL],
|
||
))
|
||
return -max(0.0, cap_decel - relax_decel)
|
||
|
||
def get_matched_follow_transition_target(self, lead, v_ego, base_t_follow, prev_output_a_target, output_a_target,
|
||
current_source, tracking_lead_active):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
low_speed_extension_active = (
|
||
bool(tracking_lead_active) and
|
||
current_source == "cruise" and
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_SPEED <= float(v_ego) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_SPEED
|
||
)
|
||
if float(v_ego) < MATCHED_FOLLOW_TRANSITION_MIN_SPEED and not low_speed_extension_active:
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
min_model_prob = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_MODEL_PROB if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MIN_MODEL_PROB
|
||
if lead_prob < min_model_prob:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
max_lead_brake = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_LEAD_BRAKE if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MAX_LEAD_BRAKE
|
||
if lead_brake > max_lead_brake:
|
||
return None
|
||
|
||
relative_speed = float(v_ego) - float(lead.vLead)
|
||
if not (STEADY_FOLLOW_BRAKE_CAP_MIN_REL_SPEED <= relative_speed <= STEADY_FOLLOW_SMOOTHING_MAX_CLOSING_SPEED):
|
||
return None
|
||
|
||
closing_speed = max(0.0, relative_speed)
|
||
max_closing_speed = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MAX_CLOSING_SPEED
|
||
if closing_speed > max_closing_speed:
|
||
return None
|
||
|
||
ttc = float(lead.dRel) / max(closing_speed, 0.1) if closing_speed > 0.1 else float("inf")
|
||
min_ttc = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TTC if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MIN_TTC
|
||
if ttc < min_ttc:
|
||
return None
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(base_t_follow)
|
||
min_headway_margin = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_HEADWAY_MARGIN if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MIN_HEADWAY_MARGIN
|
||
full_headway_margin = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_FULL_HEADWAY_MARGIN if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_FULL_HEADWAY_MARGIN
|
||
if headway_margin < min_headway_margin:
|
||
return None
|
||
if actual_headway > float(base_t_follow) + STEADY_FOLLOW_BRAKE_CAP_MAX_HEADWAY_ABOVE_TARGET:
|
||
return None
|
||
|
||
target_delta = float(output_a_target) - float(prev_output_a_target)
|
||
if low_speed_extension_active:
|
||
if float(prev_output_a_target) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TARGET:
|
||
return None
|
||
if float(output_a_target) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TARGET:
|
||
return None
|
||
if abs(target_delta) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_DELTA_A:
|
||
return None
|
||
elif abs(target_delta) < 1e-3:
|
||
return None
|
||
|
||
headway_factor = float(np.clip(
|
||
(headway_margin - min_headway_margin) /
|
||
max(full_headway_margin - min_headway_margin, 1e-3),
|
||
0.0,
|
||
1.0,
|
||
))
|
||
|
||
min_positive_step = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_POSITIVE_STEP if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MIN_POSITIVE_STEP
|
||
max_positive_step = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_POSITIVE_STEP if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MAX_POSITIVE_STEP
|
||
positive_step = float(np.interp(
|
||
max(float(lead.vLead) - float(v_ego), 0.0),
|
||
[0.0, 1.0],
|
||
[min_positive_step, max_positive_step],
|
||
))
|
||
min_negative_step = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_NEGATIVE_STEP if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MIN_NEGATIVE_STEP
|
||
max_negative_step = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MAX_NEGATIVE_STEP if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_MAX_NEGATIVE_STEP
|
||
negative_step = float(np.interp(
|
||
closing_speed,
|
||
[0.0, max_closing_speed],
|
||
[min_negative_step, max_negative_step],
|
||
))
|
||
|
||
# The more space we still have, the less abrupt the comfort path should be.
|
||
positive_step = float(np.interp(headway_factor, [0.0, 1.0], [positive_step, min_positive_step]))
|
||
negative_step = float(np.interp(headway_factor, [0.0, 1.0], [negative_step, min_negative_step]))
|
||
|
||
if float(prev_output_a_target) * float(output_a_target) < 0.0:
|
||
sign_cross_step = LOW_SPEED_MATCHED_FOLLOW_TRANSITION_SIGN_CROSS_STEP if low_speed_extension_active else MATCHED_FOLLOW_TRANSITION_SIGN_CROSS_STEP
|
||
positive_step = min(positive_step, sign_cross_step)
|
||
negative_step = min(negative_step, sign_cross_step)
|
||
|
||
lower = float(prev_output_a_target) - negative_step
|
||
upper = float(prev_output_a_target) + positive_step
|
||
smoothed_target = float(np.clip(output_a_target, lower, upper))
|
||
return smoothed_target if abs(smoothed_target - float(output_a_target)) > 1e-6 else None
|
||
|
||
def get_near_duplicate_lead_transition_target(self, lead, v_ego, base_t_follow,
|
||
prev_output_a_target, output_a_target,
|
||
current_source, tracking_lead_active):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
if current_source not in ("cruise", "lead0", "lead1"):
|
||
return None
|
||
if current_source == "cruise" and not tracking_lead_active:
|
||
return None
|
||
if not (self.lead_one.status and self.lead_two.status):
|
||
return None
|
||
identical_radar_duplicates = self.mpc.leads_share_identical_radar_track(self.lead_one, self.lead_two)
|
||
if not self.mpc.leads_are_near_duplicates(self.lead_one, self.lead_two, v_ego):
|
||
return None
|
||
low_speed_extension_active = bool(
|
||
tracking_lead_active and
|
||
current_source in ("cruise", "lead0", "lead1") and
|
||
LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_SPEED <= float(v_ego) < NEAR_DUPLICATE_LEAD_TRANSITION_MIN_SPEED
|
||
)
|
||
if float(v_ego) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_SPEED:
|
||
return None
|
||
if float(v_ego) < NEAR_DUPLICATE_LEAD_TRANSITION_MIN_SPEED and not low_speed_extension_active:
|
||
return None
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < NEAR_DUPLICATE_LEAD_TRANSITION_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > NEAR_DUPLICATE_LEAD_TRANSITION_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
relative_speed = float(v_ego) - float(lead.vLead)
|
||
closing_speed = max(0.0, relative_speed)
|
||
if closing_speed > NEAR_DUPLICATE_LEAD_TRANSITION_MAX_CLOSING_SPEED:
|
||
return None
|
||
|
||
ttc = float(lead.dRel) / max(closing_speed, 0.1) if closing_speed > 0.1 else float("inf")
|
||
if ttc < NEAR_DUPLICATE_LEAD_TRANSITION_MIN_TTC:
|
||
return None
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
if actual_headway < max(0.0, float(base_t_follow) - NEAR_DUPLICATE_LEAD_TRANSITION_MIN_HEADWAY_BELOW_TARGET):
|
||
return None
|
||
max_headway_above_target = NEAR_DUPLICATE_LEAD_TRANSITION_MAX_HEADWAY_ABOVE_TARGET
|
||
if low_speed_extension_active and identical_radar_duplicates:
|
||
max_headway_above_target += LOW_SPEED_IDENTICAL_RADAR_DUPLICATE_TRANSITION_EXTRA_HEADWAY
|
||
if actual_headway > float(base_t_follow) + max_headway_above_target:
|
||
return None
|
||
|
||
target_delta = float(output_a_target) - float(prev_output_a_target)
|
||
if abs(target_delta) < NEAR_DUPLICATE_LEAD_TRANSITION_MIN_DELTA_A:
|
||
return None
|
||
|
||
if low_speed_extension_active and (float(prev_output_a_target) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TARGET or
|
||
float(output_a_target) < LOW_SPEED_MATCHED_FOLLOW_TRANSITION_MIN_TARGET):
|
||
return None
|
||
|
||
positive_step = NEAR_DUPLICATE_LEAD_TRANSITION_POSITIVE_STEP
|
||
negative_step = NEAR_DUPLICATE_LEAD_TRANSITION_NEGATIVE_STEP
|
||
if float(prev_output_a_target) * float(output_a_target) < 0.0:
|
||
positive_step = min(positive_step, NEAR_DUPLICATE_LEAD_TRANSITION_SIGN_CROSS_STEP)
|
||
negative_step = min(negative_step, NEAR_DUPLICATE_LEAD_TRANSITION_SIGN_CROSS_STEP)
|
||
|
||
lower = float(prev_output_a_target) - negative_step
|
||
upper = float(prev_output_a_target) + positive_step
|
||
smoothed_target = float(np.clip(output_a_target, lower, upper))
|
||
return smoothed_target if abs(smoothed_target - float(output_a_target)) > 1e-6 else None
|
||
|
||
def get_mild_follow_zero_cross_guard_target(self, lead, v_ego, base_t_follow,
|
||
prev_output_a_target, output_a_target,
|
||
current_source, tracking_lead_active):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
if current_source not in ("cruise", "lead0", "lead1") and not tracking_lead_active:
|
||
return None
|
||
if not (MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_SPEED <= float(v_ego) <= MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_SPEED):
|
||
return None
|
||
|
||
target_delta = float(output_a_target) - float(prev_output_a_target)
|
||
if abs(target_delta) < MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_DELTA_A:
|
||
return None
|
||
|
||
lead_radar = bool(getattr(lead, "radar", False))
|
||
lead_prob = float(getattr(lead, "modelProb", 1.0 if lead_radar else 0.0))
|
||
if not lead_radar and lead_prob < MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
closing_speed = max(0.0, float(v_ego) - float(lead.vLead))
|
||
if closing_speed > MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_CLOSING_SPEED:
|
||
return None
|
||
|
||
ttc = float(lead.dRel) / max(closing_speed, 0.1) if closing_speed > 0.1 else float("inf")
|
||
if ttc < MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_TTC:
|
||
return None
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
headway_margin = actual_headway - float(base_t_follow)
|
||
if headway_margin < MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_HEADWAY_MARGIN:
|
||
return None
|
||
|
||
deadband = float(np.interp(
|
||
headway_margin,
|
||
[MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_HEADWAY_MARGIN, MILD_FOLLOW_ZERO_CROSS_GUARD_FULL_HEADWAY_MARGIN],
|
||
[MILD_FOLLOW_ZERO_CROSS_GUARD_MIN_DEADBAND, MILD_FOLLOW_ZERO_CROSS_GUARD_MAX_DEADBAND],
|
||
))
|
||
|
||
if float(prev_output_a_target) * float(output_a_target) < 0.0:
|
||
return 0.0
|
||
|
||
if abs(float(output_a_target)) < deadband and abs(float(prev_output_a_target)) < deadband + 0.06:
|
||
return 0.0
|
||
|
||
return None
|
||
|
||
def get_duplicate_slow_lead_brake_hold_target(self, lead, v_ego, base_t_follow,
|
||
prev_output_a_target, output_a_target,
|
||
current_source, tracking_lead_active):
|
||
if lead is None or not lead.status:
|
||
return None
|
||
if current_source not in ("cruise", "lead0", "lead1") and not tracking_lead_active:
|
||
return None
|
||
if not (self.lead_one.status and self.lead_two.status):
|
||
return None
|
||
if (
|
||
abs(float(self.lead_one.dRel) - float(self.lead_two.dRel)) > DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_DREL_DIFF or
|
||
abs(float(self.lead_one.vRel) - float(self.lead_two.vRel)) > DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_VREL_DIFF
|
||
):
|
||
return None
|
||
if float(v_ego) < DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_SPEED:
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if bool(getattr(lead, "radar", False)) or lead_prob < DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
prev_brake = max(0.0, -float(prev_output_a_target))
|
||
if prev_brake < DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_PREV_DECEL:
|
||
return None
|
||
|
||
target_delta = float(output_a_target) - float(prev_output_a_target)
|
||
if target_delta < DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_DELTA_A:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
closing_speed = max(0.0, float(v_ego) - float(lead.vLead))
|
||
if closing_speed < DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_CLOSING_SPEED:
|
||
return None
|
||
|
||
actual_headway = float(lead.dRel) / max(float(v_ego), 1e-3)
|
||
if actual_headway > float(base_t_follow) + DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_HEADWAY_ABOVE_TARGET:
|
||
return None
|
||
|
||
positive_step = float(np.interp(
|
||
closing_speed,
|
||
[DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_CLOSING_SPEED, 1.5, 4.0, 8.0],
|
||
[DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MIN_POSITIVE_STEP,
|
||
0.16,
|
||
0.22,
|
||
DUPLICATE_SLOW_LEAD_BRAKE_HOLD_MAX_POSITIVE_STEP],
|
||
))
|
||
if float(prev_output_a_target) * float(output_a_target) < 0.0:
|
||
positive_step = min(positive_step, DUPLICATE_SLOW_LEAD_BRAKE_HOLD_SIGN_CROSS_STEP)
|
||
|
||
upper = float(prev_output_a_target) + positive_step
|
||
smoothed_target = float(min(float(output_a_target), upper))
|
||
return smoothed_target if abs(smoothed_target - float(output_a_target)) > 1e-6 else None
|
||
|
||
def get_tracked_vision_model_brake_floor(self, lead, v_ego, accel_min, t_follow, model_desired):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
if float(v_ego) < TRACKED_VISION_MODEL_FLOOR_MIN_SPEED:
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < TRACKED_VISION_MODEL_FLOOR_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
model_brake = max(0.0, -float(model_desired))
|
||
if model_brake < TRACKED_VISION_MODEL_FLOOR_MIN_MODEL_DECEL:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
projected_closing_speed = max(0.0, float(v_ego) - float(lead.vLead)) + lead_brake * reaction_t
|
||
if projected_closing_speed < TRACKED_VISION_MODEL_FLOOR_MIN_CLOSING_SPEED:
|
||
return None
|
||
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
|
||
gap_margin = float(lead.dRel) - desired_gap
|
||
max_gap_margin = max(TRACKED_VISION_MODEL_FLOOR_MAX_GAP_BUFFER_MIN,
|
||
TRACKED_VISION_MODEL_FLOOR_MAX_GAP_BUFFER_GAIN * float(v_ego))
|
||
if gap_margin < TRACKED_VISION_MODEL_FLOOR_MIN_GAP_MARGIN or gap_margin > max_gap_margin:
|
||
return None
|
||
|
||
projected_ttc = float(lead.dRel) / max(projected_closing_speed, 0.1)
|
||
if projected_ttc > TRACKED_VISION_MODEL_FLOOR_MAX_TTC:
|
||
return None
|
||
|
||
floor_decel = float(np.interp(
|
||
model_brake,
|
||
[TRACKED_VISION_MODEL_FLOOR_MIN_MODEL_DECEL, 1.6],
|
||
[TRACKED_VISION_MODEL_FLOOR_MIN_DECEL, TRACKED_VISION_MODEL_FLOOR_MAX_DECEL],
|
||
))
|
||
floor_decel += float(np.interp(lead_brake, [0.0, 0.8], [0.0, TRACKED_VISION_MODEL_FLOOR_LEAD_BRAKE_MAX]))
|
||
return max(accel_min, -min(TRACKED_VISION_MODEL_FLOOR_MAX_DECEL, floor_decel))
|
||
|
||
def get_tracked_vision_model_brake_cap(self, lead, v_ego, t_follow, model_desired):
|
||
if lead is None or not lead.status or bool(getattr(lead, "radar", False)):
|
||
return None
|
||
if not (TRACKED_VISION_MODEL_CAP_MIN_SPEED <= float(v_ego) <= TRACKED_VISION_MODEL_CAP_MAX_SPEED):
|
||
return None
|
||
|
||
lead_prob = float(getattr(lead, "modelProb", 0.0))
|
||
if lead_prob < TRACKED_VISION_MODEL_CAP_MIN_MODEL_PROB:
|
||
return None
|
||
|
||
model_brake = max(0.0, -float(model_desired))
|
||
if model_brake > TRACKED_VISION_MODEL_CAP_MAX_MODEL_DECEL:
|
||
return None
|
||
|
||
lead_brake = max(0.0, -float(getattr(lead, "aLeadK", 0.0)))
|
||
if lead_brake > TRACKED_VISION_MODEL_CAP_MAX_LEAD_BRAKE:
|
||
return None
|
||
|
||
reaction_t = max(self.CP.longitudinalActuatorDelay, self.dt)
|
||
projected_closing_speed = max(0.0, float(v_ego) - float(lead.vLead)) + lead_brake * reaction_t
|
||
if not (TRACKED_VISION_MODEL_CAP_MIN_CLOSING_SPEED <= projected_closing_speed <= TRACKED_VISION_MODEL_CAP_MAX_CLOSING_SPEED):
|
||
return None
|
||
|
||
desired_gap = float(desired_follow_distance(v_ego, lead.vLead, t_follow))
|
||
gap_margin = float(lead.dRel) - desired_gap
|
||
max_gap_margin = max(TRACKED_VISION_MODEL_CAP_MAX_GAP_BUFFER_MIN,
|
||
TRACKED_VISION_MODEL_CAP_MAX_GAP_BUFFER_GAIN * float(v_ego))
|
||
if gap_margin < TRACKED_VISION_MODEL_CAP_MIN_GAP_MARGIN or gap_margin > max_gap_margin:
|
||
return None
|
||
|
||
projected_ttc = float(lead.dRel) / max(projected_closing_speed, 0.1)
|
||
if projected_ttc < TRACKED_VISION_MODEL_CAP_MIN_TTC:
|
||
return None
|
||
|
||
cap_decel = float(np.interp(
|
||
model_brake,
|
||
[0.0, TRACKED_VISION_MODEL_CAP_MAX_MODEL_DECEL],
|
||
[TRACKED_VISION_MODEL_CAP_MIN_DECEL, TRACKED_VISION_MODEL_CAP_MAX_DECEL],
|
||
))
|
||
cap_decel += float(np.interp(
|
||
projected_closing_speed,
|
||
[TRACKED_VISION_MODEL_CAP_MIN_CLOSING_SPEED, TRACKED_VISION_MODEL_CAP_MAX_CLOSING_SPEED],
|
||
[0.0, 0.08],
|
||
))
|
||
return -min(TRACKED_VISION_MODEL_CAP_MAX_DECEL, cap_decel)
|
||
|
||
@staticmethod
|
||
def raw_close_lead_needs_control(lead, v_ego):
|
||
if lead is None or not lead.status:
|
||
return False
|
||
|
||
d_rel = max(float(lead.dRel), 0.0)
|
||
lead_speed = max(float(getattr(lead, "vLead", 0.0)), 0.0)
|
||
closing_speed = float(v_ego - lead.vLead)
|
||
lead_braking = float(lead.aLeadK) < -0.5
|
||
centered_lead = abs(float(getattr(lead, "yRel", 0.0))) <= RAW_LEAD_LOW_SPEED_HOLD_MAX_LATERAL_OFFSET
|
||
if (
|
||
centered_lead and
|
||
float(v_ego) <= RAW_LEAD_LOW_SPEED_HOLD_MAX_EGO_SPEED and
|
||
lead_speed <= RAW_LEAD_LOW_SPEED_HOLD_MAX_LEAD_SPEED and
|
||
d_rel <= RAW_LEAD_LOW_SPEED_HOLD_MAX_DISTANCE and
|
||
closing_speed >= RAW_LEAD_LOW_SPEED_HOLD_MIN_CLOSING_SPEED
|
||
):
|
||
return True
|
||
|
||
if closing_speed <= RAW_LEAD_SAFETY_MIN_CLOSING_SPEED and not lead_braking:
|
||
return False
|
||
|
||
dynamic_distance = max(RAW_LEAD_SAFETY_DISTANCE, 3.0 * float(v_ego))
|
||
ttc = d_rel / max(closing_speed, 0.1) if closing_speed > 0.1 else float("inf")
|
||
return d_rel < dynamic_distance and (ttc < RAW_LEAD_SAFETY_TTC or lead_braking)
|
||
|
||
def update(self, sm, starpilot_toggles):
|
||
if self.is_preap:
|
||
self._preap_param_frame += 1
|
||
if self._preap_params is not None and (self._preap_param_frame % 20) == 0:
|
||
self.nap_adaptive_accel = self._preap_params.get_bool("NAPAdaptiveAccel")
|
||
|
||
self.generation = getattr(starpilot_toggles, "model_version", None)
|
||
self.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
|
||
self.mpc.mode = 'acc'
|
||
if not self.mlsim:
|
||
self.mpc.mode = self.mode
|
||
|
||
if len(sm['carControl'].orientationNED) == 3:
|
||
accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
|
||
else:
|
||
accel_coast = ACCEL_MAX
|
||
|
||
v_ego = get_planner_v_ego(self.CP, sm['carState'])
|
||
scene_v_ego = float(sm['carState'].vEgo)
|
||
v_cruise = sm['starpilotPlan'].vCruise
|
||
if not np.isfinite(v_cruise):
|
||
cloudlog.error(f"Longitudinal planner received non-finite vCruise={v_cruise}, falling back to v_ego={v_ego:.2f}")
|
||
v_cruise = max(v_ego, 0.0)
|
||
v_cruise_initialized = sm['carState'].vCruise != V_CRUISE_UNSET
|
||
|
||
long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
|
||
force_slow_decel = sm['controlsState'].forceDecel
|
||
|
||
# Reset current state when not engaged, or user is controlling the speed
|
||
reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['selfdriveState'].enabled
|
||
# PCM cruise speed may be updated a few cycles later, check if initialized
|
||
reset_state = reset_state or not v_cruise_initialized
|
||
|
||
# No change cost when user is controlling the speed, or when standstill
|
||
prev_accel_constraint = not (reset_state or sm['carState'].standstill)
|
||
|
||
if self.mpc.mode == 'acc':
|
||
accel_limits = [sm['starpilotPlan'].minAcceleration, sm['starpilotPlan'].maxAcceleration]
|
||
steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
|
||
accel_limits_turns = limit_accel_in_turns(v_ego, steer_angle_without_offset, accel_limits, self.CP)
|
||
accel_limits_turns[0] = max(get_vehicle_min_accel(self.CP, v_ego), accel_limits_turns[0])
|
||
else:
|
||
accel_limits = [ACCEL_MIN, ACCEL_MAX]
|
||
accel_limits_turns = [ACCEL_MIN, ACCEL_MAX]
|
||
|
||
if reset_state:
|
||
self.v_desired_filter.x = v_ego
|
||
# Clip aEgo to cruise limits to prevent large accelerations when becoming active
|
||
self.a_desired = np.clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])
|
||
self.model_allow_throttle = True
|
||
|
||
# Prevent divergence, smooth in current v_ego
|
||
self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
|
||
# Compute model v_ego error
|
||
self.v_model_error = self.get_model_speed_error(sm['modelV2'], v_ego)
|
||
x, v, a, j, throttle_prob = self.parse_model(sm['modelV2'], self.v_model_error, v_ego, starpilot_toggles)
|
||
# Don't clip at low speeds since throttle_prob doesn't account for creep. Use
|
||
# hysteresis here because raw gasPressProb noise can chatter the throttle gate.
|
||
if v_ego <= MIN_ALLOW_THROTTLE_SPEED:
|
||
self.model_allow_throttle = True
|
||
elif self.model_allow_throttle:
|
||
self.model_allow_throttle = throttle_prob > ALLOW_THROTTLE_DISABLE_THRESHOLD
|
||
else:
|
||
self.model_allow_throttle = throttle_prob > ALLOW_THROTTLE_ENABLE_THRESHOLD
|
||
self.allow_throttle = self.model_allow_throttle and not sm['starpilotPlan'].disableThrottle
|
||
|
||
if not self.allow_throttle:
|
||
clipped_accel_coast = max(accel_coast, accel_limits_turns[0])
|
||
# Hold the output cap to the physical coasting limit until throttle is
|
||
# allowed again. Relaxing back toward positive accel while the gate is
|
||
# still closed can stall downhill coastdown well above the target speed.
|
||
accel_limits_turns[1] = min(accel_limits_turns[1], clipped_accel_coast)
|
||
no_throttle_output_max = accel_limits_turns[1]
|
||
|
||
if force_slow_decel:
|
||
v_cruise = 0.0
|
||
# clip limits, cannot init MPC outside of bounds
|
||
accel_limits_turns[0] = min(accel_limits_turns[0], self.a_desired + 0.05)
|
||
accel_limits_turns[1] = max(accel_limits_turns[1], self.a_desired - 0.05)
|
||
|
||
tracking_lead = bool(sm['starpilotPlan'].trackingLead)
|
||
self.lead_one = sm['radarState'].leadOne
|
||
self.lead_two = sm['radarState'].leadTwo
|
||
raw_close_lead_control = any(self.raw_close_lead_needs_control(lead, scene_v_ego) for lead in (self.lead_one, self.lead_two))
|
||
# StarPilot trackingLead is debounce/model-length based. Keep a raw close-lead
|
||
# safety path so ACC/chill does not ignore a visible lead during that debounce.
|
||
lead_control_active = tracking_lead or raw_close_lead_control
|
||
lead_one_active = bool(self.lead_one.status and lead_control_active)
|
||
effective_t_follow = self.get_dynamic_t_follow(sm['starpilotPlan'].tFollow, self.lead_one if lead_one_active else None, v_ego)
|
||
|
||
if self.is_preap and self.nap_adaptive_accel and lead_one_active:
|
||
follow_limit = get_preap_follow_limit(v_ego)
|
||
if follow_limit is not None:
|
||
safe_dist = get_safe_obstacle_distance(v_ego, effective_t_follow)
|
||
lead_dist_ratio = float(self.lead_one.dRel) / max(safe_dist, 1.0)
|
||
cap_strength = float(np.clip(1.0 - (lead_dist_ratio - 1.2) / 0.3, 0.0, 1.0))
|
||
if cap_strength > 0.0:
|
||
accel_limits_turns[1] = min(
|
||
accel_limits_turns[1],
|
||
accel_limits_turns[1] * (1.0 - cap_strength) + follow_limit * cap_strength,
|
||
)
|
||
|
||
lead_dist = self.lead_one.dRel if lead_one_active else 50.0
|
||
|
||
# Smooth lead distance (EMA) to avoid chatter in thresholds
|
||
alpha = max(0.02, min(0.15, 0.05 + 0.002 * v_ego))
|
||
if self.lead_dist_f is None:
|
||
self.lead_dist_f = float(lead_dist)
|
||
else:
|
||
self.lead_dist_f += alpha * (float(lead_dist) - self.lead_dist_f)
|
||
|
||
# Lead stability estimation and recent-brake timer
|
||
now_t = time.monotonic()
|
||
# relative speed (ego - lead) positive when closing
|
||
v_rel = (v_ego - self.lead_one.vLead) if lead_one_active else 0.0
|
||
if self.prev_lead_dist is None:
|
||
d_rel_dot = 0.0
|
||
else:
|
||
d_rel_dot = (lead_dist - self.prev_lead_dist) / max(self.dt, 1e-3)
|
||
self.prev_lead_dist = lead_dist
|
||
|
||
# Remember time of last non-trivial model brake risk
|
||
if 'raw_brake_max' in locals() and raw_brake_max is not None and raw_brake_max > 0.02:
|
||
self.last_big_brake_t = now_t
|
||
|
||
# Stable lead heuristic (short window, cheap to compute)
|
||
recently_braked = (now_t - self.last_big_brake_t) < 0.7
|
||
self.stable_lead = (
|
||
lead_one_active and
|
||
abs(v_rel) < 0.5 and
|
||
abs(d_rel_dot) < 0.5 and
|
||
not recently_braked
|
||
)
|
||
|
||
if lead_one_active and self.mpc.source == "cruise":
|
||
lead_delta = float(self.lead_one.vLead) - float(scene_v_ego)
|
||
lead_brake = max(0.0, -float(getattr(self.lead_one, "aLeadK", 0.0)))
|
||
if (
|
||
self.is_spacious_low_closure_follow(self.lead_one, scene_v_ego, effective_t_follow) and
|
||
lead_brake <= SPACIOUS_TRACKED_FOLLOW_MAX_LEAD_BRAKE and
|
||
(
|
||
self.is_stable_post_departure_pullaway(self.lead_one, scene_v_ego, effective_t_follow) or
|
||
lead_delta >= SPACIOUS_TRACKED_FOLLOW_LATCH_MIN_LEAD_DELTA
|
||
)
|
||
):
|
||
self.spacious_follow_cap_bypass_until = now_t + SPACIOUS_TRACKED_FOLLOW_LATCH_TIME
|
||
elif not lead_one_active:
|
||
self.spacious_follow_cap_bypass_until = 0.0
|
||
|
||
# Calculate scene uncertainty from model desire prediction entropy and disengage predictions
|
||
uncertainty = 0.0
|
||
if hasattr(sm['modelV2'], 'meta'):
|
||
# Desire prediction entropy (maneuver uncertainty), normalized to [0, 1]
|
||
desire_entropy = 0.0
|
||
if hasattr(sm['modelV2'].meta, 'desirePrediction'):
|
||
desire_probs = sm['modelV2'].meta.desirePrediction
|
||
if len(desire_probs) > 1:
|
||
probs = np.asarray(desire_probs, dtype=float)
|
||
total = float(np.sum(probs))
|
||
if total > 1e-6:
|
||
p = probs / total
|
||
entropy = -np.sum(p * np.log(p + 1e-10))
|
||
max_entropy = np.log(len(p))
|
||
desire_entropy = float(entropy / max(max_entropy, 1e-6)) # normalized entropy in [0,1]
|
||
else:
|
||
desire_entropy = 0.0 # guard against all-zero vector
|
||
|
||
# Disengage prediction risk (intervention likelihood)
|
||
disengage_risk = 0.0
|
||
raw_brake_max = -1.0
|
||
lam = -1.0
|
||
if hasattr(sm['modelV2'].meta, 'disengagePredictions'):
|
||
# Use brake press probabilities as primary risk indicator
|
||
brake_probs = sm['modelV2'].meta.disengagePredictions.brakePressProbs
|
||
if len(brake_probs) > 0:
|
||
# Exponentially decayed max over the full horizon
|
||
probs = np.asarray(brake_probs, dtype=float)
|
||
# Clip tiny brake blips so they don't inflate uncertainty
|
||
if float(np.max(probs)) < 0.015:
|
||
probs = probs * 0.5
|
||
raw_brake_max = float(np.max(probs))
|
||
# Time vector assuming model horizon step = DT_MDL
|
||
t = np.arange(len(probs), dtype=float) * DT_MDL
|
||
lam = 0.6 # decay rate per second (tunable: 0.5–0.9 typical)
|
||
weights = np.exp(-lam * t)
|
||
disengage_risk = float(np.max(probs * weights))
|
||
|
||
# Combined uncertainty metric (range roughly 0..2), with dual-track filtering
|
||
raw_uncertainty = desire_entropy + disengage_risk
|
||
# Update filters
|
||
self.uncert_slow.update(raw_uncertainty)
|
||
self.uncert_fast.update(raw_uncertainty)
|
||
# Use a more permissive track for accel decisions
|
||
uncertainty = self.uncert_slow.x
|
||
uncertainty_accel = min(self.uncert_slow.x, self.uncert_fast.x)
|
||
|
||
# --- Slope-based panic bypass ---
|
||
if self._uncert_last_t is None:
|
||
uncert_slope = 0.0
|
||
else:
|
||
dt_u = max(1e-3, now_t - self._uncert_last_t)
|
||
uncert_slope = (uncertainty - self._uncert_last) / dt_u
|
||
self._uncert_last = uncertainty
|
||
self._uncert_last_t = now_t
|
||
|
||
panic_close_window = False
|
||
closing_fast = False
|
||
desired_gap = None
|
||
closing_speed = 0.0
|
||
if lead_one_active:
|
||
desired_gap = float(desired_follow_distance(v_ego, self.lead_one.vLead, effective_t_follow))
|
||
scene_desired_gap = float(desired_follow_distance(scene_v_ego, self.lead_one.vLead, effective_t_follow))
|
||
close_gap_window = max(UNCERT_PANIC_MAX_GAP_BUFFER_MIN,
|
||
UNCERT_PANIC_MAX_GAP_BUFFER_GAIN * float(v_ego))
|
||
panic_close_window = float(self.lead_one.dRel) <= scene_desired_gap + close_gap_window
|
||
closing_speed = max(0.0, scene_v_ego - self.lead_one.vLead)
|
||
closing_fast = closing_speed >= max(
|
||
UNCERT_PANIC_MIN_CLOSING_SPEED,
|
||
UNCERT_PANIC_MIN_CLOSING_SPEED_GAIN * float(scene_v_ego),
|
||
)
|
||
|
||
# Only bypass lead smoothing when we're closing meaningfully and already
|
||
# near the follow window. Far or nearly pace-matched leads should stay on
|
||
# the smoothed path so the planner doesn't flip-flop between accel and brake.
|
||
panic_bypass = panic_close_window and closing_fast and (
|
||
uncert_slope > UNCERT_SLOPE_TRIG or uncertainty >= UNCERT_MAG_TRIG
|
||
)
|
||
|
||
prioritize_smooth_following = bool(getattr(starpilot_toggles, "prioritize_smooth_following", False))
|
||
allow_complex_follow_logic = not prioritize_smooth_following
|
||
|
||
steady_follow_filter_floor = 0.0
|
||
if allow_complex_follow_logic and lead_one_active and desired_gap is not None and not panic_bypass:
|
||
lead_brake = max(0.0, -float(getattr(self.lead_one, "aLeadK", 0.0)))
|
||
lead_radar = bool(getattr(self.lead_one, "radar", False))
|
||
lead_prob = float(getattr(self.lead_one, "modelProb", 1.0 if lead_radar else 0.0))
|
||
actual_headway = float(self.lead_one.dRel) / max(scene_v_ego, 1e-3)
|
||
matched_follow_window = (
|
||
is_radarless_matched_follow_window(
|
||
scene_v_ego,
|
||
self.lead_one.dRel,
|
||
self.lead_one.vLead,
|
||
effective_t_follow,
|
||
radar=lead_radar,
|
||
lead_brake=lead_brake,
|
||
lead_prob=lead_prob,
|
||
) or (
|
||
lead_radar and
|
||
scene_v_ego >= STEADY_FOLLOW_SMOOTHING_MIN_SPEED and
|
||
STEADY_FOLLOW_SMOOTHING_MIN_CLOSING_SPEED <= closing_speed <= STEADY_FOLLOW_SMOOTHING_MAX_CLOSING_SPEED and
|
||
actual_headway >= max(STEADY_FOLLOW_SMOOTHING_MIN_HEADWAY,
|
||
effective_t_follow - STEADY_FOLLOW_SMOOTHING_HEADWAY_BELOW_TARGET) and
|
||
actual_headway <= effective_t_follow + STEADY_FOLLOW_SMOOTHING_HEADWAY_ABOVE_TARGET and
|
||
lead_brake <= STEADY_FOLLOW_SMOOTHING_MAX_LEAD_BRAKE
|
||
)
|
||
)
|
||
if matched_follow_window:
|
||
steady_follow_filter_floor = STEADY_FOLLOW_SMOOTHING_FILTER_FACTOR_FLOOR
|
||
|
||
if panic_bypass:
|
||
if now_t - self._panic_bypass_log_t > 5.0:
|
||
self._panic_bypass_log_t = now_t
|
||
try:
|
||
cloudlog.warning(
|
||
"LON_SLOPE close bypass: "
|
||
f"slope={uncert_slope:.3f}/s uncertainty={uncertainty:.3f} "
|
||
f"v_ego={v_ego:.2f} v_rel={(v_ego - self.lead_one.vLead) if lead_one_active else 0.0:.2f} "
|
||
f"lead_dist={self.lead_dist_f if self.lead_dist_f is not None else -1:.2f}"
|
||
)
|
||
except Exception:
|
||
pass
|
||
|
||
personality = get_longitudinal_personality(sm)
|
||
|
||
self.mpc.set_weights(sm['starpilotPlan'].accelerationJerk,
|
||
sm['starpilotPlan'].dangerJerk,
|
||
sm['starpilotPlan'].speedJerk,
|
||
prev_accel_constraint,
|
||
personality=personality,
|
||
v_ego=v_ego,
|
||
lead_dist=self.lead_dist_f if lead_one_active and self.lead_dist_f is not None else 50.0,
|
||
uncertainty=uncertainty,
|
||
panic_bypass=panic_bypass,
|
||
filter_time_factor_floor=steady_follow_filter_floor)
|
||
self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
|
||
self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
|
||
# After deciding the MPC mode via get_mpc_mode(), ensure MPC uses that mode when not mlsim
|
||
dec_mpc_mode = self.get_mpc_mode()
|
||
if not self.mlsim:
|
||
self.mpc.mode = dec_mpc_mode
|
||
self.mpc.update(sm['radarState'], v_cruise, x, v, a, j,
|
||
sm['starpilotPlan'].dangerFactor, effective_t_follow,
|
||
personality=personality, tracking_lead=lead_control_active,
|
||
optional_far_lead_comfort=allow_complex_follow_logic)
|
||
|
||
self.a_desired_trajectory_full = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
|
||
self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
|
||
self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
|
||
self.j_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC[:-1], self.mpc.j_solution)
|
||
|
||
# TODO counter is only needed because radar is glitchy, remove once radar is gone
|
||
self.fcw = should_publish_planner_fcw(self.mpc.crash_cnt, sm['carState'], sm['radarState'])
|
||
if self.fcw:
|
||
cloudlog.info("FCW triggered")
|
||
|
||
# Safety checks for rubber-banding mitigation
|
||
max_jerk = np.max(np.abs(self.mpc.j_solution))
|
||
max_accel_change = np.max(np.abs(np.diff(self.mpc.a_solution)))
|
||
if max_jerk > 5.0: # m/s^3
|
||
cloudlog.warning(f"High jerk detected: {max_jerk:.2f} m/s^3")
|
||
if max_accel_change > 2.0: # m/s^2
|
||
cloudlog.warning(f"High acceleration change: {max_accel_change:.2f} m/s^2")
|
||
|
||
# Interpolate 0.05 seconds and save as starting point for next iteration
|
||
a_prev = self.a_desired
|
||
self.a_desired = float(np.interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
|
||
self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0
|
||
|
||
# Anticipatory pre-brake to avoid "coming in hot" when closing on a lead
|
||
if allow_complex_follow_logic and lead_one_active:
|
||
rel_v = max(0.0, v_ego - self.lead_one.vLead)
|
||
# dynamic time headway adds a small buffer when uncertainty is elevated
|
||
base_th = max(1.6, effective_t_follow)
|
||
th = base_th + 0.6 * max(0.0, uncertainty - 0.42)
|
||
desired_gap = th * v_ego
|
||
if (self.lead_dist_f is not None and self.lead_dist_f < desired_gap and rel_v > 0.5):
|
||
k_rel, k_unc = 0.04, 0.20
|
||
pre_brake = k_rel * rel_v + k_unc * max(0.0, uncertainty - 0.42)
|
||
pre_brake = min(pre_brake, 0.06)
|
||
self.a_desired = float(self.a_desired - pre_brake)
|
||
|
||
# Small deadzone around zero accel to kill micro-dithers
|
||
if -0.05 < self.a_desired < 0.05:
|
||
self.a_desired = 0.0
|
||
|
||
classic_model = bool(getattr(starpilot_toggles, "classic_model", False))
|
||
tinygrad_model = bool(getattr(starpilot_toggles, "tinygrad_model", False))
|
||
experimental_mlsim = bool(tinygrad_model and self.mlsim and self.mode != 'acc')
|
||
action_t = self.CP.longitudinalActuatorDelay + DT_MDL
|
||
prev_output_a_target = float(self.output_a_target)
|
||
|
||
if classic_model:
|
||
output_a_target, output_should_stop = get_accel_from_plan_classic(
|
||
self.CP, self.v_desired_trajectory, self.a_desired_trajectory, starpilot_toggles.vEgoStopping)
|
||
elif tinygrad_model:
|
||
output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(
|
||
self.v_desired_trajectory, self.a_desired_trajectory,
|
||
action_t=action_t, vEgoStopping=starpilot_toggles.vEgoStopping)
|
||
output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
|
||
output_should_stop_e2e = sm['modelV2'].action.shouldStop
|
||
|
||
if self.mode == 'acc' or self.generation == 'v9':
|
||
output_a_target = output_a_target_mpc
|
||
output_should_stop = output_should_stop_mpc
|
||
else:
|
||
output_a_target = min(output_a_target_mpc, output_a_target_e2e)
|
||
output_should_stop = output_should_stop_e2e or output_should_stop_mpc
|
||
else:
|
||
output_a_target, output_should_stop = get_accel_from_plan(
|
||
self.v_desired_trajectory, self.a_desired_trajectory,
|
||
action_t=action_t, vEgoStopping=starpilot_toggles.vEgoStopping)
|
||
|
||
comfort_output_accel_min = get_vehicle_min_accel(self.CP, v_ego) if experimental_mlsim else accel_limits_turns[0]
|
||
vision_cap_accel_min = min(comfort_output_accel_min, get_vehicle_min_accel(self.CP, v_ego))
|
||
output_accel_min = comfort_output_accel_min
|
||
model_desired_accel = float(sm['modelV2'].action.desiredAcceleration)
|
||
|
||
if not tracking_lead:
|
||
pretracking_vision_caps = []
|
||
for lead in (self.lead_one, self.lead_two):
|
||
if lead.status and not bool(getattr(lead, "radar", False)):
|
||
pretracking_cap = self.get_vision_untracked_slow_lead_cap(lead, v_ego, vision_cap_accel_min)
|
||
if pretracking_cap is not None:
|
||
pretracking_vision_caps.append((pretracking_cap, lead))
|
||
|
||
if pretracking_vision_caps:
|
||
pretracking_vision_cap, pretracking_vision_lead = min(pretracking_vision_caps, key=lambda cap_and_lead: cap_and_lead[0])
|
||
lead_brake = max(0.0, -float(getattr(pretracking_vision_lead, "aLeadK", 0.0)))
|
||
immediate_pretracking_cap = (
|
||
pretracking_vision_cap <= -VISION_UNTRACKED_SLOW_LEAD_IMMEDIATE_DECEL or
|
||
float(getattr(pretracking_vision_lead, "dRel", float("inf"))) <= VISION_UNTRACKED_SLOW_LEAD_IMMEDIATE_DISTANCE or
|
||
lead_brake >= VISION_UNTRACKED_SLOW_LEAD_IMMEDIATE_LEAD_BRAKE or
|
||
float(getattr(pretracking_vision_lead, "vLead", float("inf"))) <= VISION_UNTRACKED_SLOW_LEAD_RELAXED_MAX_LEAD_SPEED
|
||
)
|
||
|
||
if immediate_pretracking_cap:
|
||
self.untracked_slow_lead_confirm_t = VISION_UNTRACKED_SLOW_LEAD_CONFIRM_TIME
|
||
else:
|
||
self.untracked_slow_lead_confirm_t = min(
|
||
self.untracked_slow_lead_confirm_t + self.dt,
|
||
VISION_UNTRACKED_SLOW_LEAD_CONFIRM_TIME,
|
||
)
|
||
|
||
if self.untracked_slow_lead_confirm_t >= VISION_UNTRACKED_SLOW_LEAD_CONFIRM_TIME:
|
||
self.a_desired = min(self.a_desired, pretracking_vision_cap)
|
||
output_a_target = min(output_a_target, pretracking_vision_cap)
|
||
else:
|
||
self.untracked_slow_lead_confirm_t = 0.0
|
||
else:
|
||
self.untracked_slow_lead_confirm_t = 0.0
|
||
|
||
close_lead_caps = []
|
||
tracked_vision_approach_caps = []
|
||
vision_low_speed_stop_active = False
|
||
vision_brake_cap_active = False
|
||
if lead_control_active:
|
||
for lead in (self.lead_one, self.lead_two):
|
||
cap = self.get_close_lead_brake_cap(lead, v_ego, output_accel_min)
|
||
if cap is not None:
|
||
close_lead_caps.append(cap)
|
||
slow_stop_cap = self.get_vision_slow_stopped_lead_cap(lead, v_ego, vision_cap_accel_min, effective_t_follow)
|
||
if slow_stop_cap is not None:
|
||
close_lead_caps.append(slow_stop_cap)
|
||
vision_brake_cap_active = True
|
||
approach_cap = self.get_vision_lead_approach_cap(lead, v_ego, vision_cap_accel_min, effective_t_follow)
|
||
if approach_cap is not None:
|
||
tracked_vision_approach_caps.append((
|
||
approach_cap,
|
||
self.tracked_vision_lead_approach_needs_immediate_brake(lead, v_ego, approach_cap),
|
||
))
|
||
low_speed_stop_cap, low_speed_stop_active = self.get_vision_low_speed_stop_buffer_cap(lead, v_ego, vision_cap_accel_min)
|
||
if low_speed_stop_cap is not None:
|
||
close_lead_caps.append(low_speed_stop_cap)
|
||
vision_brake_cap_active = True
|
||
vision_low_speed_stop_active |= low_speed_stop_active
|
||
if tracked_vision_approach_caps:
|
||
if any(immediate for _, immediate in tracked_vision_approach_caps):
|
||
self.vision_lead_approach_confirm_t = VISION_LEAD_APPROACH_CONFIRM_TIME
|
||
else:
|
||
self.vision_lead_approach_confirm_t = min(
|
||
self.vision_lead_approach_confirm_t + self.dt,
|
||
VISION_LEAD_APPROACH_CONFIRM_TIME,
|
||
)
|
||
|
||
if self.vision_lead_approach_confirm_t >= VISION_LEAD_APPROACH_CONFIRM_TIME:
|
||
close_lead_caps.append(min(cap for cap, _ in tracked_vision_approach_caps))
|
||
vision_brake_cap_active = True
|
||
else:
|
||
self.vision_lead_approach_confirm_t = 0.0
|
||
if close_lead_caps:
|
||
close_lead_brake_cap = min(close_lead_caps)
|
||
self.a_desired = min(self.a_desired, close_lead_brake_cap)
|
||
output_a_target = min(output_a_target, close_lead_brake_cap)
|
||
|
||
standstill_nudge_gap = max(float(getattr(starpilot_toggles, "stop_distance", STOP_DISTANCE)), STOP_DISTANCE) - 0.5
|
||
moving_leads = [lead for lead in (self.lead_one, self.lead_two)
|
||
if lead.status and
|
||
lead.vLead > STANDSTILL_LEAD_NUDGE_MIN_SPEED and lead.dRel >= standstill_nudge_gap]
|
||
accelerating_nudge_lead = any(
|
||
lead.status and
|
||
float(getattr(lead, "vLead", 0.0)) > STANDSTILL_LEAD_NUDGE_MIN_SPEED and
|
||
float(getattr(lead, "aLeadK", 0.0)) >= STANDSTILL_LEAD_NUDGE_MIN_LEAD_ACCEL and
|
||
float(getattr(lead, "dRel", 0.0)) >= standstill_nudge_gap
|
||
for lead in (self.lead_one, self.lead_two)
|
||
)
|
||
confident_depart_detected = any(self.is_confident_lead_depart(lead, float(sm['carState'].vEgo))
|
||
for lead in (self.lead_one, self.lead_two))
|
||
lead_depart_ready = any(
|
||
lead.status and
|
||
lead.vLead >= STANDSTILL_LEAD_DEPART_MIN_LEAD_SPEED and
|
||
lead.dRel >= standstill_nudge_gap + STANDSTILL_LEAD_DEPART_MIN_GAP_MARGIN
|
||
for lead in (self.lead_one, self.lead_two)
|
||
)
|
||
depart_safety_veto = (not bool(getattr(starpilot_toggles, "radar_takeoffs", False))
|
||
and self.has_offcenter_radar_depart_conflict(sm))
|
||
if (
|
||
lead_control_active and
|
||
sm['carState'].standstill and
|
||
not depart_safety_veto and
|
||
not bool(getattr(sm['starpilotPlan'], 'forcingStop', False)) and
|
||
not bool(getattr(sm['starpilotPlan'], 'redLight', False)) and
|
||
confident_depart_detected
|
||
):
|
||
self.confident_lead_depart_elapsed = min(
|
||
LEAD_DEPART_CONFIDENT_CONFIRM_TIME,
|
||
self.confident_lead_depart_elapsed + self.dt,
|
||
)
|
||
else:
|
||
self.confident_lead_depart_elapsed = 0.0
|
||
confident_depart_ready = (
|
||
confident_depart_detected and
|
||
self.confident_lead_depart_elapsed >= LEAD_DEPART_CONFIDENT_CONFIRM_TIME
|
||
)
|
||
|
||
standstill_stopped_lead_guard_cap = None
|
||
standstill_guard_lead_present = any(bool(getattr(lead, "status", False)) for lead in (self.lead_one, self.lead_two))
|
||
if standstill_guard_lead_present and (bool(sm['carState'].standstill) or float(sm['carState'].vEgo) <= STANDSTILL_STOPPED_LEAD_GUARD_MAX_EGO_SPEED):
|
||
release_ready = bool(lead_depart_ready or confident_depart_ready)
|
||
standstill_stopped_lead_guard_caps = [
|
||
cap for cap in (
|
||
self.get_standstill_stopped_lead_guard_cap(
|
||
self.lead_one,
|
||
float(sm['carState'].vEgo),
|
||
output_accel_min,
|
||
standstill_nudge_gap,
|
||
release_ready,
|
||
confident_depart_ready,
|
||
),
|
||
self.get_standstill_stopped_lead_guard_cap(
|
||
self.lead_two,
|
||
float(sm['carState'].vEgo),
|
||
output_accel_min,
|
||
standstill_nudge_gap,
|
||
release_ready,
|
||
confident_depart_ready,
|
||
),
|
||
) if cap is not None
|
||
]
|
||
if standstill_stopped_lead_guard_caps:
|
||
standstill_stopped_lead_guard_cap = min(standstill_stopped_lead_guard_caps)
|
||
output_should_stop = True
|
||
|
||
if lead_control_active and sm['carState'].standstill and moving_leads and not depart_safety_veto:
|
||
output_a_target = max(output_a_target, STANDSTILL_LEAD_NUDGE_ACCEL)
|
||
|
||
if (
|
||
lead_control_active and
|
||
sm['carState'].standstill and
|
||
(confident_depart_ready or lead_depart_ready) and
|
||
not depart_safety_veto and
|
||
not bool(getattr(sm['starpilotPlan'], 'forcingStop', False)) and
|
||
not bool(getattr(sm['starpilotPlan'], 'redLight', False)) and
|
||
(confident_depart_ready or model_desired_accel >= STANDSTILL_LEAD_DEPART_MIN_MODEL_ACCEL)
|
||
):
|
||
vision_low_speed_stop_active = False
|
||
output_should_stop = False
|
||
output_a_target = max(output_a_target, STANDSTILL_LEAD_DEPART_MIN_ACCEL)
|
||
self.post_departure_follow_settle_until = now_t + POST_DEPARTURE_FOLLOW_SETTLE_LATCH_TIME
|
||
|
||
if lead_control_active and lead_depart_ready and not depart_safety_veto and not output_should_stop and float(sm['carState'].vEgo) <= STANDSTILL_LEAD_DEPART_MAX_EGO_SPEED:
|
||
output_a_target = max(output_a_target, STANDSTILL_LEAD_DEPART_MIN_ACCEL)
|
||
self.post_departure_follow_settle_until = now_t + POST_DEPARTURE_FOLLOW_SETTLE_LATCH_TIME
|
||
|
||
if depart_safety_veto or output_should_stop or bool(getattr(sm['starpilotPlan'], 'forcingStop', False)) or bool(getattr(sm['starpilotPlan'], 'redLight', False)):
|
||
self.lead_depart_accel_hold_until = 0.0
|
||
|
||
lead_depart_accel_floor = None
|
||
if lead_control_active and not output_should_stop and not depart_safety_veto:
|
||
lead_depart_accel_floors = [
|
||
floor for floor in (
|
||
self.get_lead_depart_accel_floor(self.lead_one, scene_v_ego, model_desired_accel),
|
||
self.get_lead_depart_accel_floor(self.lead_two, scene_v_ego, model_desired_accel),
|
||
) if floor is not None
|
||
]
|
||
if lead_depart_accel_floors:
|
||
lead_depart_accel_floor = max(lead_depart_accel_floors)
|
||
if sm['carState'].standstill:
|
||
self.lead_depart_accel_hold_until = now_t + LEAD_DEPART_ACCEL_HOLD_TIME
|
||
|
||
lead_depart_accel_hold_active = (
|
||
lead_depart_accel_floor is not None and
|
||
now_t < self.lead_depart_accel_hold_until and
|
||
float(sm['carState'].vEgo) <= LEAD_DEPART_ACCEL_HOLD_MAX_EGO_SPEED
|
||
)
|
||
|
||
low_speed_weak_lead_accel_cap = None
|
||
if not output_should_stop:
|
||
low_speed_weak_lead_accel_caps = [
|
||
cap for cap in (
|
||
self.get_low_speed_weak_lead_accel_cap(self.lead_one, scene_v_ego),
|
||
self.get_low_speed_weak_lead_accel_cap(self.lead_two, scene_v_ego),
|
||
) if cap is not None
|
||
]
|
||
if low_speed_weak_lead_accel_caps:
|
||
low_speed_weak_lead_accel_cap = min(low_speed_weak_lead_accel_caps)
|
||
|
||
close_stop_active = bool(output_should_stop or vision_low_speed_stop_active)
|
||
|
||
close_stop_hold_cap = None
|
||
if lead_control_active and close_stop_active:
|
||
close_stop_hold_caps = [
|
||
cap for cap in (
|
||
self.get_vision_close_stop_hold_cap(self.lead_one, v_ego, output_accel_min, close_stop_active),
|
||
self.get_vision_close_stop_hold_cap(self.lead_two, v_ego, output_accel_min, close_stop_active),
|
||
) if cap is not None
|
||
]
|
||
if close_stop_hold_caps:
|
||
close_stop_hold_cap = min(close_stop_hold_caps)
|
||
|
||
close_release_hold_cap = None
|
||
if lead_control_active and not close_stop_active:
|
||
close_release_hold_caps = [
|
||
cap for cap in (
|
||
self.get_vision_close_release_hold_cap(self.lead_one, v_ego, vision_cap_accel_min, close_stop_active),
|
||
self.get_vision_close_release_hold_cap(self.lead_two, v_ego, vision_cap_accel_min, close_stop_active),
|
||
) if cap is not None
|
||
]
|
||
if close_release_hold_caps:
|
||
close_release_hold_cap = min(close_release_hold_caps)
|
||
|
||
close_settle_guard_active = bool(
|
||
output_should_stop or
|
||
vision_low_speed_stop_active or
|
||
sm['controlsState'].longControlState == LongCtrlState.stopping
|
||
)
|
||
close_settle_cap = None
|
||
if lead_control_active:
|
||
close_settle_caps = [
|
||
cap for cap in (
|
||
self.get_vision_close_settle_cap(self.lead_one, v_ego, output_accel_min, close_settle_guard_active),
|
||
self.get_vision_close_settle_cap(self.lead_two, v_ego, output_accel_min, close_settle_guard_active),
|
||
) if cap is not None
|
||
]
|
||
if close_settle_caps:
|
||
close_settle_cap = min(close_settle_caps)
|
||
|
||
close_final_guard_cap = None
|
||
if lead_control_active:
|
||
close_final_guard_caps = [
|
||
cap for cap in (
|
||
self.get_vision_close_final_guard_cap(self.lead_one, v_ego, output_accel_min),
|
||
self.get_vision_close_final_guard_cap(self.lead_two, v_ego, output_accel_min),
|
||
) if cap is not None
|
||
]
|
||
if close_final_guard_caps:
|
||
close_final_guard_cap = min(close_final_guard_caps)
|
||
|
||
if allow_complex_follow_logic and lead_one_active:
|
||
lead_catchup_accel_cap = self.get_lead_catchup_accel_cap(
|
||
self.lead_one,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
current_source=self.mpc.source,
|
||
tracking_lead_active=tracking_lead,
|
||
)
|
||
if lead_catchup_accel_cap is not None:
|
||
self.a_desired = min(self.a_desired, lead_catchup_accel_cap)
|
||
output_a_target = min(output_a_target, lead_catchup_accel_cap)
|
||
|
||
if lead_control_active and np.isfinite(v_cruise) and any(lead.status for lead in (self.lead_one, self.lead_two)):
|
||
# Keep follow/catchup behavior from pulling past the cruise target. Using the
|
||
# same action horizon as the planner preserves normal accel farther below set speed.
|
||
cruise_accel_cap = (v_cruise - v_ego + 0.01) / max(action_t, self.dt)
|
||
output_a_target = min(output_a_target, cruise_accel_cap)
|
||
|
||
if vision_brake_cap_active:
|
||
output_accel_min = min(output_accel_min, vision_cap_accel_min)
|
||
|
||
follow_control_lead = None
|
||
if allow_complex_follow_logic:
|
||
follow_control_lead = self.get_follow_control_lead(
|
||
lead_control_active,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
allow_optional_far_lead_logic=True,
|
||
)
|
||
if allow_complex_follow_logic and follow_control_lead is not None and not panic_bypass:
|
||
if not output_should_stop and not vision_low_speed_stop_active:
|
||
tracked_vision_model_brake_floor = self.get_tracked_vision_model_brake_floor(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
output_accel_min,
|
||
effective_t_follow,
|
||
model_desired_accel,
|
||
)
|
||
if tracked_vision_model_brake_floor is not None:
|
||
self.a_desired = min(self.a_desired, tracked_vision_model_brake_floor)
|
||
output_a_target = min(output_a_target, tracked_vision_model_brake_floor)
|
||
|
||
if allow_complex_follow_logic:
|
||
matched_follow_brake_cap = self.get_matched_follow_brake_cap(follow_control_lead, scene_v_ego, effective_t_follow)
|
||
if matched_follow_brake_cap is not None:
|
||
self.a_desired = max(self.a_desired, matched_follow_brake_cap)
|
||
output_a_target = max(output_a_target, matched_follow_brake_cap)
|
||
|
||
if not close_lead_caps and not output_should_stop and not vision_low_speed_stop_active:
|
||
low_speed_transition_brake_cap = self.get_low_speed_follow_transition_brake_cap(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
prev_output_a_target,
|
||
output_a_target,
|
||
)
|
||
if low_speed_transition_brake_cap is not None:
|
||
self.a_desired = max(self.a_desired, low_speed_transition_brake_cap)
|
||
output_a_target = max(output_a_target, low_speed_transition_brake_cap)
|
||
|
||
comfort_lead = self.lead_two if self.mpc.source == 'lead1' and self.lead_two.status else self.lead_one
|
||
if allow_complex_follow_logic and comfort_lead is not None and not panic_bypass:
|
||
far_lead_brake_cap = self.get_far_lead_brake_cap(comfort_lead, scene_v_ego, effective_t_follow)
|
||
if far_lead_brake_cap is not None:
|
||
self.a_desired = max(self.a_desired, far_lead_brake_cap)
|
||
output_a_target = max(output_a_target, far_lead_brake_cap)
|
||
|
||
if allow_complex_follow_logic and follow_control_lead is not None and not panic_bypass and not output_should_stop and not vision_low_speed_stop_active:
|
||
tracked_vision_model_brake_cap = self.get_tracked_vision_model_brake_cap(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
model_desired_accel,
|
||
)
|
||
if tracked_vision_model_brake_cap is not None:
|
||
self.a_desired = max(self.a_desired, tracked_vision_model_brake_cap)
|
||
output_a_target = max(output_a_target, tracked_vision_model_brake_cap)
|
||
|
||
if allow_complex_follow_logic and follow_control_lead is not None and not panic_bypass and not output_should_stop and not vision_low_speed_stop_active:
|
||
matched_follow_transition_target = self.get_matched_follow_transition_target(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
prev_output_a_target,
|
||
output_a_target,
|
||
self.mpc.source,
|
||
bool(getattr(sm["starpilotPlan"], "trackingLead", False)),
|
||
)
|
||
if matched_follow_transition_target is not None:
|
||
if matched_follow_transition_target < output_a_target:
|
||
self.a_desired = min(self.a_desired, matched_follow_transition_target)
|
||
else:
|
||
self.a_desired = max(self.a_desired, matched_follow_transition_target)
|
||
output_a_target = matched_follow_transition_target
|
||
|
||
if allow_complex_follow_logic and comfort_lead is not None and not panic_bypass and not output_should_stop and not vision_low_speed_stop_active:
|
||
near_duplicate_transition_target = self.get_near_duplicate_lead_transition_target(
|
||
comfort_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
prev_output_a_target,
|
||
output_a_target,
|
||
self.mpc.source,
|
||
bool(getattr(sm["starpilotPlan"], "trackingLead", False)),
|
||
)
|
||
if near_duplicate_transition_target is not None:
|
||
if near_duplicate_transition_target < output_a_target:
|
||
self.a_desired = min(self.a_desired, near_duplicate_transition_target)
|
||
else:
|
||
self.a_desired = max(self.a_desired, near_duplicate_transition_target)
|
||
output_a_target = near_duplicate_transition_target
|
||
|
||
duplicate_slow_lead_brake_hold_target = self.get_duplicate_slow_lead_brake_hold_target(
|
||
comfort_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
prev_output_a_target,
|
||
output_a_target,
|
||
self.mpc.source,
|
||
bool(getattr(sm["starpilotPlan"], "trackingLead", False)),
|
||
)
|
||
if duplicate_slow_lead_brake_hold_target is not None:
|
||
if duplicate_slow_lead_brake_hold_target < output_a_target:
|
||
self.a_desired = min(self.a_desired, duplicate_slow_lead_brake_hold_target)
|
||
else:
|
||
self.a_desired = max(self.a_desired, duplicate_slow_lead_brake_hold_target)
|
||
output_a_target = duplicate_slow_lead_brake_hold_target
|
||
|
||
if allow_complex_follow_logic and follow_control_lead is not None and not panic_bypass and not output_should_stop and not vision_low_speed_stop_active:
|
||
cruise_tracking_lead_accel_cap = self.get_cruise_tracking_lead_accel_cap(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
self.mpc.source,
|
||
tracking_lead,
|
||
)
|
||
if cruise_tracking_lead_accel_cap is not None:
|
||
self.a_desired = min(self.a_desired, cruise_tracking_lead_accel_cap)
|
||
output_a_target = min(output_a_target, cruise_tracking_lead_accel_cap)
|
||
|
||
cruise_tracking_lead_transition_target = self.get_cruise_tracking_lead_accel_transition_target(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
prev_output_a_target,
|
||
output_a_target,
|
||
self.mpc.source,
|
||
)
|
||
if cruise_tracking_lead_transition_target is not None:
|
||
self.a_desired = min(self.a_desired, cruise_tracking_lead_transition_target)
|
||
output_a_target = min(output_a_target, cruise_tracking_lead_transition_target)
|
||
|
||
mild_follow_zero_cross_guard_target = self.get_mild_follow_zero_cross_guard_target(
|
||
follow_control_lead,
|
||
scene_v_ego,
|
||
effective_t_follow,
|
||
prev_output_a_target,
|
||
output_a_target,
|
||
self.mpc.source,
|
||
tracking_lead,
|
||
)
|
||
if mild_follow_zero_cross_guard_target is not None:
|
||
output_a_target = mild_follow_zero_cross_guard_target
|
||
|
||
output_accel_max = no_throttle_output_max if not self.allow_throttle else accel_limits_turns[1]
|
||
output_a_target = float(np.clip(output_a_target, output_accel_min, output_accel_max))
|
||
|
||
if close_stop_hold_cap is not None:
|
||
self.a_desired = min(self.a_desired, close_stop_hold_cap)
|
||
output_a_target = min(output_a_target, close_stop_hold_cap)
|
||
|
||
if standstill_stopped_lead_guard_cap is not None:
|
||
self.a_desired = min(self.a_desired, standstill_stopped_lead_guard_cap)
|
||
output_a_target = min(output_a_target, standstill_stopped_lead_guard_cap)
|
||
|
||
if close_settle_cap is not None:
|
||
self.a_desired = min(self.a_desired, close_settle_cap)
|
||
output_a_target = min(output_a_target, close_settle_cap)
|
||
|
||
if close_final_guard_cap is not None:
|
||
self.a_desired = min(self.a_desired, close_final_guard_cap)
|
||
output_a_target = min(output_a_target, close_final_guard_cap)
|
||
|
||
if close_release_hold_cap is not None:
|
||
self.a_desired = min(self.a_desired, close_release_hold_cap)
|
||
output_a_target = min(output_a_target, close_release_hold_cap)
|
||
|
||
if depart_safety_veto:
|
||
self.a_desired = min(self.a_desired, 0.0)
|
||
output_a_target = min(output_a_target, 0.0)
|
||
if sm['carState'].standstill:
|
||
output_should_stop = True
|
||
|
||
if lead_depart_accel_hold_active:
|
||
output_a_target = max(output_a_target, lead_depart_accel_floor)
|
||
|
||
if low_speed_weak_lead_accel_cap is not None:
|
||
self.a_desired = min(self.a_desired, low_speed_weak_lead_accel_cap)
|
||
output_a_target = min(output_a_target, low_speed_weak_lead_accel_cap)
|
||
|
||
if (
|
||
lead_control_active and
|
||
(bool(sm['carState'].standstill) or float(sm['carState'].vEgo) <= STANDSTILL_STOPPED_LEAD_GUARD_MAX_EGO_SPEED) and
|
||
output_should_stop and
|
||
accelerating_nudge_lead and
|
||
not depart_safety_veto
|
||
):
|
||
output_a_target = max(output_a_target, STANDSTILL_LEAD_NUDGE_ACCEL)
|
||
|
||
force_stop_handoff = bool(
|
||
getattr(sm['starpilotPlan'], 'forcingStop', False) and
|
||
not lead_control_active and
|
||
(
|
||
float(getattr(sm['starpilotPlan'], 'forcingStopLength', float('inf'))) < 1.0 or
|
||
float(getattr(sm['starpilotPlan'], 'vCruise', float('inf'))) <= FORCE_STOP_HANDOFF_MAX_VCRUISE
|
||
)
|
||
)
|
||
|
||
if force_stop_handoff:
|
||
output_should_stop = True
|
||
|
||
manual_stop_resume_override = self._update_manual_stop_resume_override(sm)
|
||
if manual_stop_resume_override:
|
||
output_a_target = max(output_a_target, MANUAL_STOP_RESUME_OVERRIDE_MIN_ACCEL)
|
||
output_should_stop = False
|
||
|
||
self.output_a_target = output_a_target
|
||
self.output_should_stop = bool(output_should_stop or vision_low_speed_stop_active)
|
||
|
||
def publish(self, sm, pm):
|
||
plan_send = messaging.new_message('longitudinalPlan')
|
||
|
||
plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'selfdriveState', 'radarState'])
|
||
|
||
longitudinalPlan = plan_send.longitudinalPlan
|
||
longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
|
||
longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
|
||
longitudinalPlan.solverExecutionTime = self.mpc.solve_time
|
||
|
||
longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
|
||
longitudinalPlan.accels = self.a_desired_trajectory.tolist()
|
||
longitudinalPlan.jerks = self.j_desired_trajectory.tolist()
|
||
|
||
longitudinalPlan.hasLead = sm['radarState'].leadOne.status
|
||
longitudinalPlan.longitudinalPlanSource = self.mpc.source
|
||
longitudinalPlan.fcw = self.fcw
|
||
|
||
longitudinalPlan.aTarget = float(self.output_a_target)
|
||
force_stop_handoff = bool(
|
||
sm['starpilotPlan'].forcingStop and (
|
||
sm['starpilotPlan'].forcingStopLength < 1.0 or
|
||
sm['starpilotPlan'].vCruise <= FORCE_STOP_HANDOFF_MAX_VCRUISE
|
||
)
|
||
)
|
||
longitudinalPlan.shouldStop = bool(self.output_should_stop) or force_stop_handoff
|
||
longitudinalPlan.allowBrake = True
|
||
longitudinalPlan.allowThrottle = bool(self.allow_throttle)
|
||
|
||
pm.send('longitudinalPlan', plan_send)
|