mirror of
https://github.com/firestar5683/StarPilot.git
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FrogAi
e6bfab3fc6
Added toggles for nudgeless lane changes, lane detection, and one lane change per signal activation with a lane change delay factor.
238 lines
12 KiB
Python
238 lines
12 KiB
Python
import numpy as np
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import cereal.messaging as messaging
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from cereal import log
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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.common.params import Params
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from openpilot.selfdrive.car.interfaces import ACCEL_MIN, ACCEL_MAX
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from openpilot.selfdrive.controls.lib.desire_helper import LANE_CHANGE_SPEED_MIN
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from openpilot.selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import A_CHANGE_COST, J_EGO_COST, COMFORT_BRAKE, STOP_DISTANCE, get_jerk_factor, \
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get_safe_obstacle_distance, get_stopped_equivalence_factor, get_T_FOLLOW
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from openpilot.selfdrive.controls.lib.longitudinal_planner import A_CRUISE_MIN, Lead, get_max_accel
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from openpilot.system.version import get_short_branch
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from openpilot.selfdrive.frogpilot.controls.lib.conditional_experimental_mode import ConditionalExperimentalMode
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from openpilot.selfdrive.frogpilot.controls.lib.frogpilot_functions import CITY_SPEED_LIMIT, CRUISING_SPEED, STAGING_BRANCHES, calculate_lane_width, calculate_road_curvature
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from openpilot.selfdrive.frogpilot.controls.lib.map_turn_speed_controller import MapTurnSpeedController
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from openpilot.selfdrive.frogpilot.controls.lib.model_manager import RADARLESS_MODELS
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# Acceleration profiles - Credit goes to the DragonPilot team!
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# MPH = [0., 18, 36, 63, 94]
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A_CRUISE_MIN_BP_CUSTOM = [0., 8., 16., 28., 42.]
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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 = [-0.001, -0.010, -0.28, -0.56, -0.56]
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A_CRUISE_MAX_VALS_ECO = [3.5, 3.2, 2.3, 2.0, 1.15, .80, .58, .36, .30, .091]
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A_CRUISE_MIN_VALS_SPORT = [-0.50, -0.52, -0.55, -0.57, -0.60]
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A_CRUISE_MAX_VALS_SPORT = [3.5, 3.5, 3.3, 2.8, 1.5, 1.0, .75, .6, .38, .2]
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def get_min_accel_eco(v_ego):
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return interp(v_ego, A_CRUISE_MIN_BP_CUSTOM, A_CRUISE_MIN_VALS_ECO)
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def get_max_accel_eco(v_ego):
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return interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_ECO)
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def get_min_accel_sport(v_ego):
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return interp(v_ego, A_CRUISE_MIN_BP_CUSTOM, A_CRUISE_MIN_VALS_SPORT)
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def get_max_accel_sport(v_ego):
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return interp(v_ego, A_CRUISE_MAX_BP_CUSTOM, A_CRUISE_MAX_VALS_SPORT)
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class FrogPilotPlanner:
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def __init__(self, CP):
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self.CP = CP
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self.params = Params()
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self.params_memory = Params("/dev/shm/params")
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self.cem = ConditionalExperimentalMode()
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self.lead_one = Lead()
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self.mtsc = MapTurnSpeedController()
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self.staging = get_short_branch() in STAGING_BRANCHES
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self.radarless_model = self.params.get("Model", block=True, encoding='utf-8') in RADARLESS_MODELS
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self.jerk = 0
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self.mtsc_target = 0
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self.t_follow = 0
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def update(self, carState, controlsState, frogpilotCarControl, frogpilotNavigation, liveLocationKalman, modelData, radarState):
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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 = max(carState.vEgo, 0)
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v_lead = self.lead_one.vLead
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if self.acceleration_profile == 1:
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self.max_accel = get_max_accel_eco(v_ego)
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elif self.acceleration_profile in (2, 3):
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self.max_accel = get_max_accel_sport(v_ego)
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elif not controlsState.experimentalMode:
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self.max_accel = get_max_accel(v_ego)
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else:
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self.max_accel = ACCEL_MAX
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v_cruise_changed = self.mtsc_target < v_cruise
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if self.deceleration_profile == 1 and not v_cruise_changed:
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self.min_accel = get_min_accel_eco(v_ego)
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elif self.deceleration_profile == 2 and not v_cruise_changed:
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self.min_accel = get_min_accel_sport(v_ego)
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elif not controlsState.experimentalMode:
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self.min_accel = A_CRUISE_MIN
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else:
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self.min_accel = ACCEL_MIN
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check_lane_width = self.adjacent_lanes or self.blind_spot_path or self.lane_detection
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if check_lane_width and v_ego >= LANE_CHANGE_SPEED_MIN:
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self.lane_width_left = float(calculate_lane_width(modelData.laneLines[0], modelData.laneLines[1], modelData.roadEdges[0]))
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self.lane_width_right = float(calculate_lane_width(modelData.laneLines[3], modelData.laneLines[2], modelData.roadEdges[1]))
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else:
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self.lane_width_left = 0
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self.lane_width_right = 0
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road_curvature = calculate_road_curvature(modelData, v_ego)
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if self.lead_one.status and self.CP.openpilotLongitudinalControl:
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base_jerk = get_jerk_factor(self.custom_personalities, self.aggressive_jerk, self.standard_jerk, self.relaxed_jerk, controlsState.personality)
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base_t_follow = get_T_FOLLOW(self.custom_personalities, self.aggressive_follow, self.standard_follow, self.relaxed_follow, controlsState.personality)
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self.safe_obstacle_distance = int(np.mean(get_safe_obstacle_distance(v_ego, self.t_follow)))
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self.safe_obstacle_distance_stock = int(np.mean(get_safe_obstacle_distance(v_ego, base_t_follow)))
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self.stopped_equivalence_factor = int(np.mean(get_stopped_equivalence_factor(v_lead)))
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self.jerk, self.t_follow = self.update_follow_values(base_jerk, self.lead_one, base_t_follow, v_ego, v_lead)
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else:
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self.safe_obstacle_distance = 0
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self.safe_obstacle_distance_stock = 0
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self.stopped_equivalence_factor = 0
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self.t_follow = 1.45
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self.v_cruise = self.update_v_cruise(carState, controlsState, controlsState.enabled, liveLocationKalman, modelData, road_curvature, v_cruise, v_ego)
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if self.conditional_experimental_mode and self.CP.openpilotLongitudinalControl or self.green_light_alert:
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self.cem.update(carState, controlsState.enabled, frogpilotNavigation, self.lead_one, modelData, road_curvature, self.t_follow, v_ego)
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if self.radarless_model:
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model_leads = list(modelData.leadsV3)
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if len(model_leads) > 0:
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model_lead = model_leads[0]
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self.lead_one.update(model_lead.x[0], model_lead.y[0], model_lead.v[0], model_lead.a[0], model_lead.prob)
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else:
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self.lead_one.reset()
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else:
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self.lead_one = radarState.leadOne
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def update_follow_values(self, jerk, lead_one, t_follow, v_ego, v_lead):
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stopping_distance = STOP_DISTANCE + max(self.increased_stopping_distance - v_ego, 0)
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lead_distance = self.lead_one.dRel + stopping_distance
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# Offset by FrogAi for FrogPilot for a more natural takeoff with a lead
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if self.aggressive_acceleration and not self.release:
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distance_factor = np.maximum(1, lead_distance - (v_ego * t_follow))
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standstill_offset = max(stopping_distance - v_ego, 0)
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acceleration_offset = np.clip((v_lead - v_ego) + standstill_offset - COMFORT_BRAKE, 1, distance_factor)
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jerk /= acceleration_offset
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t_follow /= acceleration_offset
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elif self.aggressive_acceleration:
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distance_factor = np.maximum(1, lead_distance - (v_lead * t_follow))
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standstill_offset = max(STOP_DISTANCE - (v_ego**COMFORT_BRAKE), 0)
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acceleration_offset = np.clip((v_lead - v_ego) + standstill_offset - COMFORT_BRAKE, 1, distance_factor)
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t_follow /= acceleration_offset
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return jerk, t_follow
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def update_v_cruise(self, carState, controlsState, enabled, liveLocationKalman, modelData, road_curvature, v_cruise, v_ego):
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gps_check = (liveLocationKalman.status == log.LiveLocationKalman.Status.valid) and liveLocationKalman.positionGeodetic.valid and liveLocationKalman.gpsOK
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v_cruise_cluster = max(controlsState.vCruiseCluster, controlsState.vCruise) * CV.KPH_TO_MS
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v_cruise_diff = v_cruise_cluster - v_cruise
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v_ego_cluster = max(carState.vEgoCluster, v_ego)
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v_ego_diff = v_ego_cluster - v_ego
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# Pfeiferj's Map Turn Speed Controller
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if self.map_turn_speed_controller and v_ego > CRUISING_SPEED and enabled and gps_check:
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mtsc_active = self.mtsc_target < v_cruise
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self.mtsc_target = np.clip(self.mtsc.target_speed(v_ego, carState.aEgo), CRUISING_SPEED, v_cruise)
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if self.mtsc_curvature_check and road_curvature < 1.0 and not mtsc_active:
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self.mtsc_target = v_cruise
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if self.mtsc_target == CRUISING_SPEED:
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self.mtsc_target = v_cruise
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else:
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self.mtsc_target = v_cruise
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targets = [self.mtsc_target]
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filtered_targets = [target if target > CRUISING_SPEED else v_cruise for target in targets]
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return min(filtered_targets)
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def publish(self, sm, pm):
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frogpilot_plan_send = messaging.new_message('frogpilotPlan')
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frogpilot_plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
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frogpilotPlan = frogpilot_plan_send.frogpilotPlan
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frogpilotPlan.accelerationJerk = A_CHANGE_COST * (float(self.jerk) if self.lead_one.status else 1)
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frogpilotPlan.accelerationJerkStock = A_CHANGE_COST
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frogpilotPlan.adjustedCruise = float(self.mtsc_target * (CV.MS_TO_KPH if self.is_metric else CV.MS_TO_MPH))
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frogpilotPlan.conditionalExperimental = self.cem.experimental_mode
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frogpilotPlan.desiredFollowDistance = self.safe_obstacle_distance - self.stopped_equivalence_factor
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frogpilotPlan.egoJerk = J_EGO_COST * (float(self.jerk) if self.lead_one.status else 1)
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frogpilotPlan.egoJerkStock = J_EGO_COST
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frogpilotPlan.jerk = float(self.jerk)
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frogpilotPlan.safeObstacleDistance = self.safe_obstacle_distance
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frogpilotPlan.safeObstacleDistanceStock = self.safe_obstacle_distance_stock
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frogpilotPlan.stoppedEquivalenceFactor = self.stopped_equivalence_factor
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frogpilotPlan.laneWidthLeft = self.lane_width_left
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frogpilotPlan.laneWidthRight = self.lane_width_right
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frogpilotPlan.minAcceleration = self.min_accel
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frogpilotPlan.maxAcceleration = self.max_accel
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frogpilotPlan.tFollow = float(self.t_follow)
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frogpilotPlan.vCruise = float(self.v_cruise)
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frogpilotPlan.redLight = self.cem.red_light_detected
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pm.send('frogpilotPlan', frogpilot_plan_send)
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def update_frogpilot_params(self):
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self.is_metric = self.params.get_bool("IsMetric")
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self.conditional_experimental_mode = self.CP.openpilotLongitudinalControl and self.params.get_bool("ConditionalExperimental")
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if self.conditional_experimental_mode:
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self.cem.update_frogpilot_params()
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custom_alerts = self.params.get_bool("CustomAlerts")
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self.green_light_alert = custom_alerts and self.params.get_bool("GreenLightAlert")
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self.custom_personalities = self.params.get_bool("CustomPersonalities")
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self.aggressive_jerk = self.params.get_float("AggressiveJerk")
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self.aggressive_follow = self.params.get_float("AggressiveFollow")
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self.standard_jerk = self.params.get_float("StandardJerk")
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self.standard_follow = self.params.get_float("StandardFollow")
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self.relaxed_jerk = self.params.get_float("RelaxedJerk")
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self.relaxed_follow = self.params.get_float("RelaxedFollow")
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custom_ui = self.params.get_bool("CustomUI")
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self.adjacent_lanes = custom_ui and self.params.get_bool("AdjacentPath")
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self.blind_spot_path = custom_ui and self.params.get_bool("BlindSpotPath")
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nudgeless_lane_change = self.params.get_bool("NudgelessLaneChange")
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self.lane_detection = nudgeless_lane_change and self.params.get_int("LaneDetectionWidth") != 0
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longitudinal_tune = self.CP.openpilotLongitudinalControl and self.params.get_bool("LongitudinalTune")
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self.acceleration_profile = self.params.get_int("AccelerationProfile") if longitudinal_tune else 0
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self.deceleration_profile = self.params.get_int("DecelerationProfile") if longitudinal_tune else 0
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self.aggressive_acceleration = longitudinal_tune and self.params.get_bool("AggressiveAcceleration")
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self.increased_stopping_distance = self.params.get_int("StoppingDistance") * (1 if self.is_metric else CV.FOOT_TO_METER) if longitudinal_tune else 0
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self.map_turn_speed_controller = self.CP.openpilotLongitudinalControl and self.params.get_bool("MTSCEnabled")
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self.mtsc_curvature_check = self.map_turn_speed_controller and self.params.get_bool("MTSCCurvatureCheck")
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self.params_memory.put_float("MapTargetLatA", 2 * (self.params.get_int("MTSCAggressiveness") / 100))
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