118 lines
5.5 KiB
Python
118 lines
5.5 KiB
Python
import cereal.messaging as messaging
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import numpy as np
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from openpilot.common.conversions import Conversions as CV
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from openpilot.common.numpy_fast import interp
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, V_CRUISE_MAX
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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.longitudinal_planner import A_CRUISE_MIN, A_CRUISE_MAX_VALS, A_CRUISE_MAX_BP, get_max_accel
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.frogpilot.functions.conditional_experimental_mode import ConditionalExperimentalMode
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# Acceleration profiles - Credit goes to the DragonPilot team!
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# MPH = [0., 35, 35, 40, 40, 45, 45, 67, 67, 67, 123]
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A_CRUISE_MIN_BP_CUSTOM = [0., 2.0, 2.01, 11., 11.01, 18., 18.01, 28., 28.01, 33., 55.]
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# MPH = [0., 6.71, 13.4, 17.9, 24.6, 33.6, 44.7, 55.9, 67.1, 123]
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A_CRUISE_MAX_BP_CUSTOM = [0., 3, 6., 8., 11., 15., 20., 25., 30., 55.]
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A_CRUISE_MIN_VALS_ECO_TUNE = [-0.480, -0.480, -0.40, -0.40, -0.40, -0.36, -0.32, -0.28, -0.28, -0.25, -0.25]
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A_CRUISE_MAX_VALS_ECO_TUNE = [3.5, 3.3, 1.7, 1.1, .76, .62, .47, .36, .28, .09]
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A_CRUISE_MIN_VALS_SPORT_TUNE = [-0.500, -0.500, -0.42, -0.42, -0.42, -0.42, -0.40, -0.35, -0.35, -0.30, -0.30]
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A_CRUISE_MAX_VALS_SPORT_TUNE = [3.5, 3.5, 3.0, 2.6, 1.4, 1.0, 0.7, 0.6, .38, .2]
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def get_min_accel_eco_tune(v_ego):
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return interp(v_ego, A_CRUISE_MIN_BP_CUSTOM, A_CRUISE_MIN_VALS_ECO_TUNE)
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def get_max_accel_eco_tune(v_ego):
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return interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_ECO_TUNE)
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def get_min_accel_sport_tune(v_ego):
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return interp(v_ego, A_CRUISE_MIN_BP_CUSTOM, A_CRUISE_MIN_VALS_SPORT_TUNE)
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def get_max_accel_sport_tune(v_ego):
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return interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT_TUNE)
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class FrogPilotPlanner:
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def __init__(self, params):
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self.cem = ConditionalExperimentalMode()
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self.v_cruise = 0
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self.x_desired_trajectory = np.zeros(CONTROL_N)
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self.update_frogpilot_params(params)
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def update(self, sm, mpc):
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carState, controlsState, modelData = sm['carState'], sm['controlsState'], sm['modelV2']
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enabled = controlsState.enabled
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v_cruise_kph = min(controlsState.vCruise, V_CRUISE_MAX)
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v_cruise = v_cruise_kph * CV.KPH_TO_MS
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v_ego = carState.vEgo
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# Acceleration profiles
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if self.acceleration_profile == 1:
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self.accel_limits = [get_min_accel_eco_tune(v_ego), get_max_accel_eco_tune(v_ego)]
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elif self.acceleration_profile in (2, 3):
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self.accel_limits = [get_min_accel_sport_tune(v_ego), get_max_accel_sport_tune(v_ego)]
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else:
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self.accel_limits = [A_CRUISE_MIN, get_max_accel(v_ego)]
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# Conditional Experimental Mode
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if self.conditional_experimental_mode and enabled:
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self.cem.update(carState, sm['frogpilotNavigation'], sm['modelV2'], mpc, sm['radarState'], carState.standstill, v_ego)
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self.v_cruise = self.update_v_cruise(carState, controlsState, modelData, enabled, v_cruise, v_ego)
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self.x_desired_trajectory_full = np.interp(ModelConstants.T_IDXS, T_IDXS_MPC, mpc.x_solution)
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self.x_desired_trajectory = self.x_desired_trajectory_full[:CONTROL_N]
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def update_v_cruise(self, carState, controlsState, modelData, enabled, v_cruise, v_ego):
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v_ego_diff = max(carState.vEgoRaw - carState.vEgoCluster, 0)
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return v_cruise - v_ego_diff
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def publish_lateral(self, sm, pm, DH):
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frogpilot_lateral_plan_send = messaging.new_message('frogpilotLateralPlan')
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frogpilot_lateral_plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'modelV2'])
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frogpilotLateralPlan = frogpilot_lateral_plan_send.frogpilotLateralPlan
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pm.send('frogpilotLateralPlan', frogpilot_lateral_plan_send)
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def publish_longitudinal(self, sm, pm, mpc):
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frogpilot_longitudinal_plan_send = messaging.new_message('frogpilotLongitudinalPlan')
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frogpilot_longitudinal_plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
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frogpilotLongitudinalPlan = frogpilot_longitudinal_plan_send.frogpilotLongitudinalPlan
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frogpilotLongitudinalPlan.conditionalExperimental = self.cem.experimental_mode
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frogpilotLongitudinalPlan.distances = self.x_desired_trajectory.tolist()
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pm.send('frogpilotLongitudinalPlan', frogpilot_longitudinal_plan_send)
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def update_frogpilot_params(self, params):
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self.is_metric = params.get_bool("IsMetric")
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self.conditional_experimental_mode = params.get_bool("ConditionalExperimental")
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if self.conditional_experimental_mode:
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self.cem.update_frogpilot_params(self.is_metric, params)
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if not params.get_bool("ExperimentalMode"):
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params.put_bool("ExperimentalMode", True)
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self.custom_personalities = params.get_bool("CustomPersonalities")
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self.aggressive_follow = params.get_int("AggressiveFollow") / 10
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self.standard_follow = params.get_int("StandardFollow") / 10
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self.relaxed_follow = params.get_int("RelaxedFollow") / 10
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self.aggressive_jerk = params.get_int("AggressiveJerk") / 10
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self.standard_jerk = params.get_int("StandardJerk") / 10
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self.relaxed_jerk = params.get_int("RelaxedJerk") / 10
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lateral_tune = params.get_bool("LateralTune")
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self.average_desired_curvature = params.get_bool("AverageCurvature") and lateral_tune
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longitudinal_tune = params.get_bool("LongitudinalTune")
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self.acceleration_profile = params.get_int("AccelerationProfile") if longitudinal_tune else 0
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self.aggressive_acceleration = params.get_bool("AggressiveAcceleration") and longitudinal_tune
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