firestar5683
170 lines
7.1 KiB
Python
170 lines
7.1 KiB
Python
import numpy as np
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from cereal import car
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from openpilot.common.realtime import DT_CTRL
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
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from openpilot.common.pid import PIDController
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from opendbc.car.gm.values import CarControllerParams, GMFlags
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CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
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LongCtrlState = car.CarControl.Actuators.LongControlState
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def long_control_state_trans(CP, active, long_control_state, v_ego,
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should_stop, brake_pressed, cruise_standstill, frogpilot_toggles):
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# Ignore cruise standstill if car has a gas interceptor.
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cruise_standstill = cruise_standstill and not CP.enableGasInterceptorDEPRECATED
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stopping_condition = should_stop
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starting_condition = (not should_stop and
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not cruise_standstill and
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not brake_pressed)
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started_condition = v_ego > frogpilot_toggles.vEgoStarting
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if not active:
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long_control_state = LongCtrlState.off
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else:
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if long_control_state == LongCtrlState.off:
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if not starting_condition:
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long_control_state = LongCtrlState.stopping
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else:
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if starting_condition and CP.startingState:
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long_control_state = LongCtrlState.starting
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else:
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long_control_state = LongCtrlState.pid
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elif long_control_state == LongCtrlState.stopping:
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if starting_condition and CP.startingState:
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long_control_state = LongCtrlState.starting
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elif starting_condition:
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long_control_state = LongCtrlState.pid
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elif long_control_state in [LongCtrlState.starting, LongCtrlState.pid]:
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if stopping_condition:
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long_control_state = LongCtrlState.stopping
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elif started_condition:
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long_control_state = LongCtrlState.pid
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return long_control_state
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class LongControl:
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def __init__(self, CP):
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self.CP = CP
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self.long_control_state = LongCtrlState.off
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self.experimental_mode = False
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self.pid = PIDController((CP.longitudinalTuning.kpBP, CP.longitudinalTuning.kpV),
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(CP.longitudinalTuning.kiBP, CP.longitudinalTuning.kiV),
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rate=1 / DT_CTRL)
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# Preserve legacy behavior when no feedforward gain is provided (default of 0.0).
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kf = getattr(CP.longitudinalTuning, 'kfDEPRECATED', 0.0)
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self.feedforward_gain = kf if kf != 0.0 else 1.0
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self._mode_setup()
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self.last_output_accel = 0.0
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self.last_a_target = 0.0
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self.integrator_hold_frames = 0
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self.is_gm_pedal_long = bool(
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CP.brand == "gm" and CP.enableGasInterceptorDEPRECATED and (CP.flags & GMFlags.PEDAL_LONG.value)
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)
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def update_mpc_mode(self, experimental_mode: bool):
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new_mode = 'blended' if experimental_mode else 'acc'
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if self.transitioning and self.prev_mode == 'blended' and self.current_mode == 'acc':
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self.mode_transition_timer = 0.0
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if new_mode != self.current_mode:
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self.prev_mode = self.current_mode
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self.transitioning = True
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self.mode_transition_timer = 0.0
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self.mode_transition_filter.x = self.last_output_accel
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self.current_mode = new_mode
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if self.transitioning:
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self.mode_transition_timer += DT_CTRL
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if self.mode_transition_timer >= self.mode_transition_duration:
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self.transitioning = False
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def _mode_setup(self):
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self.prev_mode = 'acc'
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self.current_mode = 'acc'
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self.mode_transition_filter = FirstOrderFilter(0.0, 0.5, DT_CTRL)
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self.mode_transition_timer = 0.0
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self.mode_transition_duration = 1.0
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self.transitioning = False
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def reset(self):
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self.pid.reset()
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self.last_a_target = 0.0
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self.integrator_hold_frames = 0
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def _get_pedal_long_freeze(self, a_target, error, v_ego, accel_limits):
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if not self.is_gm_pedal_long:
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self.last_a_target = a_target
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self.integrator_hold_frames = 0
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return False
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handoff_threshold = np.interp(v_ego, [0.0, 4.0, 12.0, 25.0], [0.35, 0.45, 0.55, 0.70])
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if abs(a_target - self.last_a_target) > handoff_threshold:
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hold_frames = int(round(np.interp(v_ego, [0.0, 4.0, 12.0, 25.0], [25.0, 20.0, 14.0, 10.0])))
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self.integrator_hold_frames = max(self.integrator_hold_frames, hold_frames)
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self.last_a_target = a_target
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if self.integrator_hold_frames > 0:
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self.integrator_hold_frames -= 1
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sat_buffer = 0.03
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at_neg_sat = self.last_output_accel <= (accel_limits[0] + sat_buffer)
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at_pos_sat = self.last_output_accel >= (accel_limits[1] - sat_buffer)
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sat_pushing_lower = at_neg_sat and error < -0.05
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sat_pushing_upper = at_pos_sat and error > 0.05
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return self.integrator_hold_frames > 0 or sat_pushing_lower or sat_pushing_upper
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def update(self, active, CS, a_target, should_stop, accel_limits, frogpilot_toggles):
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"""Update longitudinal control. This updates the state machine and runs a PID loop"""
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self.pid.neg_limit = accel_limits[0]
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self.pid.pos_limit = accel_limits[1]
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self.long_control_state = long_control_state_trans(self.CP, active, self.long_control_state, CS.vEgo,
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should_stop, CS.brakePressed,
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CS.cruiseState.standstill, frogpilot_toggles)
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if self.long_control_state == LongCtrlState.off:
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self.reset()
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output_accel = 0.
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elif self.long_control_state == LongCtrlState.stopping:
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output_accel = self.last_output_accel
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if output_accel > frogpilot_toggles.stopAccel:
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output_accel = min(output_accel, 0.0)
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output_accel -= frogpilot_toggles.stoppingDecelRate * DT_CTRL
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self.reset()
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elif self.long_control_state == LongCtrlState.starting:
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output_accel = (a_target if frogpilot_toggles.human_acceleration else frogpilot_toggles.startAccel)
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self.reset()
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else: # LongCtrlState.pid
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error = a_target - CS.aEgo
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self.update_mpc_mode(self.experimental_mode)
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freeze_integrator = self._get_pedal_long_freeze(a_target, error, CS.vEgo, accel_limits)
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raw_output_accel = self.pid.update(error, speed=CS.vEgo,
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feedforward=a_target * self.feedforward_gain,
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freeze_integrator=freeze_integrator)
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if self.transitioning and self.prev_mode == 'acc' and self.current_mode == 'blended':
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if raw_output_accel < 0 and raw_output_accel < self.last_output_accel:
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progress = min(1.0, self.mode_transition_timer / self.mode_transition_duration)
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# Soften transition at low urgency, but keep sharp for high decel.
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urgency_denom = max(1e-3, abs(CarControllerParams.ACCEL_MIN))
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urgency = abs(raw_output_accel / urgency_denom)
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urgency_smooth = min(1.0, urgency ** 0.4)
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blend_factor = 1.0 - (1.0 - progress) * (1.0 - urgency_smooth)
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output_accel = self.last_output_accel + (raw_output_accel - self.last_output_accel) * blend_factor
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else:
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output_accel = raw_output_accel
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else:
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output_accel = raw_output_accel
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self.last_output_accel = np.clip(output_accel, accel_limits[0], accel_limits[1])
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return self.last_output_accel
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