firestar5683
356 lines
15 KiB
Python
Executable File
356 lines
15 KiB
Python
Executable File
#!/usr/bin/env python3
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import math
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import time
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import numpy as np
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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.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 T_IDXS as T_IDXS_MPC
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, get_accel_from_plan
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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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from openpilot.frogpilot.common.frogpilot_variables import MINIMUM_LATERAL_ACCELERATION
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LON_MPC_STEP = 0.2 # first step is 0.2s
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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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MIN_ALLOW_THROTTLE_SPEED = 2.5
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# Uncertainty-based filter disable thresholds
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UNCERT_SLOPE_TRIG = 0.12 # per second
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UNCERT_MAG_TRIG = 0.50
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# Lookup table for turns
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_A_TOTAL_MAX_V = [1.7, 3.2]
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_A_TOTAL_MAX_BP = [20., 40.]
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def get_max_accel(v_ego):
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return np.interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
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def get_coast_accel(pitch):
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return np.sin(pitch) * -5.65 - 0.3 # fitted from data using xx/projects/allow_throttle/compute_coast_accel.py
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def limit_accel_in_turns(v_ego, angle_steers, a_target, CP):
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"""
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This function returns a limited long acceleration allowed, depending on the existing lateral acceleration
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this should avoid accelerating when losing the target in turns
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"""
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# FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel
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# The lookup table for turns should also be updated if we do this
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a_total_max = np.interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
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a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
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if abs(a_y) > MINIMUM_LATERAL_ACCELERATION:
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a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))
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else:
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a_x_allowed = a_target[1]
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return [a_target[0], min(a_target[1], a_x_allowed)]
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class LongitudinalPlanner:
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def __init__(self, CP, init_v=0.0, init_a=0.0, dt=DT_MDL):
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self.CP = CP
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self.mpc = LongitudinalMpc(dt=dt)
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self.fcw = False
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self.dt = dt
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self.allow_throttle = True
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self.mode = 'acc'
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self.generation = None
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self.a_desired = init_a
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self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
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self.prev_accel_clip = [ACCEL_MIN, ACCEL_MAX]
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self.output_a_target = 0.0
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self.output_should_stop = False
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self.v_desired_trajectory = np.zeros(CONTROL_N)
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self.a_desired_trajectory = np.zeros(CONTROL_N)
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self.j_desired_trajectory = np.zeros(CONTROL_N)
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self.solverExecutionTime = 0.0
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self.uncert_slow = FirstOrderFilter(0.0, 1.6, self.dt)
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self.uncert_fast = FirstOrderFilter(0.0, 0.9, self.dt)
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self.prev_lead_dist = None
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self.last_big_brake_t = 0.0
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self.stable_lead = False
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self.lead_dist_f = None
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self._uncert_last = 0.0
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self._uncert_last_t = None
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@property
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def mlsim(self):
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return self.generation in ("v8", "v10", "v11", "v12")
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@staticmethod
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def get_model_speed_error(model_msg, v_ego):
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if len(model_msg.temporalPose.trans):
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return float(np.clip(model_msg.temporalPose.trans[0] - v_ego, -5.0, 5.0))
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if len(model_msg.velocity.x) == ModelConstants.IDX_N:
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return float(np.clip(model_msg.velocity.x[0] - v_ego, -5.0, 5.0))
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return 0.0
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@staticmethod
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def parse_model(model_msg, model_error, v_ego, frogpilot_toggles):
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if (len(model_msg.position.x) == ModelConstants.IDX_N and
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len(model_msg.velocity.x) == ModelConstants.IDX_N and
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len(model_msg.acceleration.x) == ModelConstants.IDX_N):
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x = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC
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v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x) - model_error
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a = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.acceleration.x)
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j = np.zeros(len(T_IDXS_MPC))
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else:
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x = np.zeros(len(T_IDXS_MPC))
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v = np.zeros(len(T_IDXS_MPC))
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a = np.zeros(len(T_IDXS_MPC))
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j = np.zeros(len(T_IDXS_MPC))
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if len(model_msg.meta.disengagePredictions.gasPressProbs) > 1:
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throttle_prob = model_msg.meta.disengagePredictions.gasPressProbs[1]
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else:
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throttle_prob = 1.0
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# FrogPilot variables
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if frogpilot_toggles.taco_tune:
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max_lat_accel = np.interp(v_ego, [5, 10, 20], [1.5, 2.0, 3.0])
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curvatures = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.orientationRate.z) / np.clip(v, 0.3, 100.0)
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max_v = np.sqrt(max_lat_accel / (np.abs(curvatures) + 1e-3)) - 2.0
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v = np.minimum(max_v, v)
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return x, v, a, j, throttle_prob
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def update(self, sm, frogpilot_toggles):
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self.generation = getattr(frogpilot_toggles, "model_version", None)
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self.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
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self.mpc.mode = 'acc'
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if not self.mlsim:
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self.mpc.mode = self.mode
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if len(sm['carControl'].orientationNED) == 3:
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accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
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else:
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accel_coast = ACCEL_MAX
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v_ego = max(sm['carState'].vEgo, sm['carState'].vEgoCluster)
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v_cruise = sm['frogpilotPlan'].vCruise
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v_cruise_initialized = sm['carState'].vCruise != V_CRUISE_UNSET
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long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
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force_slow_decel = sm['controlsState'].forceDecel
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# Reset current state when not engaged, or user is controlling the speed
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reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['selfdriveState'].enabled
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# PCM cruise speed may be updated a few cycles later, check if initialized
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reset_state = reset_state or not v_cruise_initialized
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# No change cost when user is controlling the speed, or when standstill
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prev_accel_constraint = not (reset_state or sm['carState'].standstill)
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if self.mpc.mode == 'acc':
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accel_clip = [sm['frogpilotPlan'].minAcceleration, sm['frogpilotPlan'].maxAcceleration]
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steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
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if not sm['frogpilotPlan'].cscControllingSpeed:
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accel_clip = limit_accel_in_turns(v_ego, steer_angle_without_offset, accel_clip, self.CP)
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else:
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accel_clip = [ACCEL_MIN, ACCEL_MAX]
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if reset_state:
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self.v_desired_filter.x = v_ego
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# Clip aEgo to cruise limits to prevent large accelerations when becoming active
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self.a_desired = np.clip(sm['carState'].aEgo, accel_clip[0], accel_clip[1])
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# Prevent divergence, smooth in current v_ego
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self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
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model_error = self.get_model_speed_error(sm['modelV2'], v_ego)
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x, v, a, j, throttle_prob = self.parse_model(sm['modelV2'], model_error, v_ego, frogpilot_toggles)
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# Don't clip at low speeds since throttle_prob doesn't account for creep
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self.allow_throttle = throttle_prob > ALLOW_THROTTLE_THRESHOLD or v_ego <= MIN_ALLOW_THROTTLE_SPEED
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self.allow_throttle &= not sm['frogpilotPlan'].disableThrottle
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if not self.allow_throttle:
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clipped_accel_coast = max(accel_coast, accel_clip[0])
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clipped_accel_coast_interp = np.interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [accel_clip[1], clipped_accel_coast])
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accel_clip[1] = min(accel_clip[1], clipped_accel_coast_interp)
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if force_slow_decel:
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v_cruise = 0.0
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accel_clip[0] = min(accel_clip[0], self.a_desired + 0.05)
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accel_clip[1] = max(accel_clip[1], self.a_desired - 0.05)
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lead_one = sm['radarState'].leadOne
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lead_dist = lead_one.dRel if lead_one.status else 50.0
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alpha = max(0.02, min(0.15, 0.05 + 0.002 * v_ego))
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if self.lead_dist_f is None:
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self.lead_dist_f = float(lead_dist)
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else:
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self.lead_dist_f += alpha * (float(lead_dist) - self.lead_dist_f)
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now_t = time.monotonic()
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v_rel = (v_ego - lead_one.vLead) if lead_one.status else 0.0
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if self.prev_lead_dist is None:
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d_rel_dot = 0.0
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else:
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d_rel_dot = (lead_dist - self.prev_lead_dist) / max(self.dt, 1e-3)
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self.prev_lead_dist = lead_dist
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uncertainty = 0.0
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raw_brake_max = 0.0
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if hasattr(sm['modelV2'], 'meta'):
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desire_entropy = 0.0
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if hasattr(sm['modelV2'].meta, 'desirePrediction'):
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desire_probs = sm['modelV2'].meta.desirePrediction
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if len(desire_probs) > 1:
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probs = np.asarray(desire_probs, dtype=float)
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total = float(np.sum(probs))
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if total > 1e-6:
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p = probs / total
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entropy = -np.sum(p * np.log(p + 1e-10))
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max_entropy = np.log(len(p))
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desire_entropy = float(entropy / max(max_entropy, 1e-6))
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disengage_risk = 0.0
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if hasattr(sm['modelV2'].meta, 'disengagePredictions'):
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brake_probs = sm['modelV2'].meta.disengagePredictions.brakePressProbs
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if len(brake_probs) > 0:
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probs = np.asarray(brake_probs, dtype=float)
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if float(np.max(probs)) < 0.015:
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probs = probs * 0.5
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raw_brake_max = float(np.max(probs))
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t = np.arange(len(probs), dtype=float) * DT_MDL
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lam = 0.6
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weights = np.exp(-lam * t)
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disengage_risk = float(np.max(probs * weights))
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raw_uncertainty = desire_entropy + disengage_risk
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self.uncert_slow.update(raw_uncertainty)
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self.uncert_fast.update(raw_uncertainty)
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uncertainty = self.uncert_slow.x
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if raw_brake_max > 0.02:
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self.last_big_brake_t = now_t
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recently_braked = (now_t - self.last_big_brake_t) < 0.7
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self.stable_lead = (
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lead_one.status and
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abs(v_rel) < 0.5 and
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abs(d_rel_dot) < 0.5 and
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not recently_braked
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)
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if self._uncert_last_t is None:
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uncert_slope = 0.0
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else:
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dt_u = max(1e-3, now_t - self._uncert_last_t)
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uncert_slope = (uncertainty - self._uncert_last) / dt_u
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self._uncert_last = uncertainty
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self._uncert_last_t = now_t
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closing_fast = lead_one.status and (v_ego - lead_one.vLead) > 0.5
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panic_bypass = closing_fast and (uncert_slope > UNCERT_SLOPE_TRIG or uncertainty >= UNCERT_MAG_TRIG)
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if panic_bypass:
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cloudlog.error(f"LON_SLOPE slope={uncert_slope:.3f} uncertainty={uncertainty:.3f} v_ego={v_ego:.2f}")
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self.mpc.set_weights(sm['frogpilotPlan'].accelerationJerk,
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sm['frogpilotPlan'].dangerJerk,
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sm['frogpilotPlan'].speedJerk,
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prev_accel_constraint,
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personality=sm['selfdriveState'].personality,
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v_ego=v_ego,
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lead_dist=self.lead_dist_f if self.lead_dist_f is not None else lead_dist,
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uncertainty=uncertainty,
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panic_bypass=panic_bypass,
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stop_distance=getattr(frogpilot_toggles, "stop_distance", 6.0))
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self.mpc.set_accel_limits(accel_clip[0], accel_clip[1])
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self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
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try:
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tracking_lead = bool(sm['frogpilotPlan'].trackingLead)
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except AttributeError:
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# Backward-compatible fallback for stale schema instances.
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tracking_lead = sm['frogpilotPlan'].desiredFollowDistance > 0
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self.mpc.update(sm['radarState'], v_cruise, x, v, a, j,
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sm['frogpilotPlan'].dangerFactor, sm['frogpilotPlan'].tFollow,
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personality=sm['selfdriveState'].personality, tracking_lead=tracking_lead)
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self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
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self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
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self.j_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC[:-1], self.mpc.j_solution)
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# TODO counter is only needed because radar is glitchy, remove once radar is gone
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self.fcw = self.mpc.crash_cnt > 2 and not sm['carState'].standstill
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if self.fcw:
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cloudlog.info("FCW triggered")
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# Interpolate 0.05 seconds and save as starting point for next iteration
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a_prev = self.a_desired
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self.a_desired = float(np.interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
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self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0
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if lead_one.status:
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rel_v = max(0.0, v_ego - lead_one.vLead)
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base_th = 1.6
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th = base_th + 0.6 * max(0.0, uncertainty - 0.42)
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desired_gap = th * v_ego
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if self.lead_dist_f is not None and self.lead_dist_f < desired_gap and rel_v > 0.5:
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k_rel, k_unc = 0.04, 0.20
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pre_brake = k_rel * rel_v + k_unc * max(0.0, uncertainty - 0.42)
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self.a_desired = float(self.a_desired - min(pre_brake, 0.06))
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if -0.05 < self.a_desired < 0.05:
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self.a_desired = 0.0
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action_t = frogpilot_toggles.longitudinalActuatorDelay + DT_MDL
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output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
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action_t=action_t, vEgoStopping=frogpilot_toggles.vEgoStopping)
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output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
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output_should_stop_e2e = sm['modelV2'].action.shouldStop
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# Keep StarPilot behavior: for tinygrad v10/v11/v12 in experimental mode, blend with model action output.
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if self.mode == 'acc' or self.generation == 'v9':
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output_a_target = output_a_target_mpc
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self.output_should_stop = output_should_stop_mpc
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else:
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output_a_target = min(output_a_target_mpc, output_a_target_e2e)
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self.output_should_stop = output_should_stop_e2e or output_should_stop_mpc
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for idx in range(2):
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accel_clip[idx] = np.clip(accel_clip[idx], self.prev_accel_clip[idx] - 0.05, self.prev_accel_clip[idx] + 0.05)
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self.output_a_target = np.clip(output_a_target, accel_clip[0], accel_clip[1])
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self.prev_accel_clip = accel_clip
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def publish(self, sm, pm):
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plan_send = messaging.new_message('longitudinalPlan')
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plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'selfdriveState', 'radarState'])
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longitudinalPlan = plan_send.longitudinalPlan
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longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
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longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
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longitudinalPlan.solverExecutionTime = self.mpc.solve_time
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longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
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longitudinalPlan.accels = self.a_desired_trajectory.tolist()
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longitudinalPlan.jerks = self.j_desired_trajectory.tolist()
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longitudinalPlan.hasLead = sm['radarState'].leadOne.status
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longitudinalPlan.longitudinalPlanSource = self.mpc.source
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longitudinalPlan.fcw = self.fcw
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longitudinalPlan.aTarget = float(self.output_a_target)
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longitudinalPlan.shouldStop = bool(self.output_should_stop) or sm['frogpilotPlan'].forcingStopLength < 1
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longitudinalPlan.allowBrake = True
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longitudinalPlan.allowThrottle = bool(self.allow_throttle)
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pm.send('longitudinalPlan', plan_send)
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