mirror of
https://github.com/firestar5683/StarPilot.git
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FrogAi
24140ab24d
Automatically adjust the max speed to match the current speed limit using 'Open Street Maps', 'Navigate On openpilot', or your car's dashboard (Toyotas/Lexus/HKG only). Credit goes to Pfeiferj! https: //github.com/pfeiferj Co-Authored-By: Jacob Pfeifer <jacob@pfeifer.dev>
101 lines
4.9 KiB
Python
101 lines
4.9 KiB
Python
from openpilot.common.params import Params
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.frogpilot.controls.lib.frogpilot_functions import MovingAverageCalculator
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from openpilot.selfdrive.frogpilot.controls.lib.frogpilot_variables import CITY_SPEED_LIMIT, PROBABILITY
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from openpilot.selfdrive.frogpilot.controls.lib.speed_limit_controller import SpeedLimitController
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MODEL_LENGTH = ModelConstants.IDX_N
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PLANNER_TIME = ModelConstants.T_IDXS[MODEL_LENGTH - 1]
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class ConditionalExperimentalMode:
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def __init__(self, FrogPilotPlanner):
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self.params_memory = Params("/dev/shm/params")
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self.frogpilot_planner = FrogPilotPlanner
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self.curvature_mac = MovingAverageCalculator()
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self.slow_lead_mac = MovingAverageCalculator()
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self.stop_light_mac = MovingAverageCalculator()
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self.curve_detected = False
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self.experimental_mode = False
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self.stop_light_detected = False
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def update(self, carState, frogpilotNavigation, modelData, v_ego, v_lead, frogpilot_toggles):
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if frogpilot_toggles.experimental_mode_via_press:
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self.status_value = self.params_memory.get_int("CEStatus")
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else:
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self.status_value = 0
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if self.status_value not in {1, 2, 3, 4, 5, 6} and not carState.standstill:
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self.update_conditions(self.frogpilot_planner.tracking_lead, v_ego, v_lead, frogpilot_toggles)
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self.experimental_mode = self.check_conditions(carState, frogpilotNavigation, modelData, self.frogpilot_planner.tracking_lead, v_ego, v_lead, frogpilot_toggles)
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self.params_memory.put_int("CEStatus", self.status_value if self.experimental_mode else 0)
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else:
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self.experimental_mode = self.status_value in {2, 4, 6} or carState.standstill and self.experimental_mode
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def check_conditions(self, carState, frogpilotNavigation, modelData, tracking_lead, v_ego, v_lead, frogpilot_toggles):
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below_speed = frogpilot_toggles.conditional_limit > v_ego >= 1 and not tracking_lead
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below_speed_with_lead = frogpilot_toggles.conditional_limit_lead > v_ego >= 1 and tracking_lead
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if below_speed or below_speed_with_lead:
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self.status_value = 7 if tracking_lead else 8
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return True
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if frogpilot_toggles.conditional_signal and v_ego < CITY_SPEED_LIMIT and (carState.leftBlinker or carState.rightBlinker):
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self.status_value = 9
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return True
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approaching_maneuver = modelData.navEnabled and (frogpilotNavigation.approachingIntersection or frogpilotNavigation.approachingTurn)
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if frogpilot_toggles.conditional_navigation and approaching_maneuver and (frogpilot_toggles.conditional_navigation_lead or not tracking_lead):
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self.status_value = 10 if frogpilotNavigation.approachingIntersection else 11
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return True
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if frogpilot_toggles.conditional_curves and self.curve_detected and (frogpilot_toggles.conditional_curves_lead or not tracking_lead):
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self.status_value = 12
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return True
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if frogpilot_toggles.conditional_lead and self.slow_lead_detected:
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self.status_value = 13 if v_lead < 1 else 14
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return True
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if frogpilot_toggles.conditional_stop_lights and self.stop_light_detected:
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self.status_value = 15 if not self.frogpilot_planner.forcing_stop else 16
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return True
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if SpeedLimitController.experimental_mode:
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self.status_value = 17
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return True
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return False
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def update_conditions(self, tracking_lead, v_ego, v_lead, frogpilot_toggles):
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self.curve_detection(v_ego, frogpilot_toggles)
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self.slow_lead(tracking_lead, v_lead, frogpilot_toggles)
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self.stop_sign_and_light(tracking_lead, v_ego, frogpilot_toggles)
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def curve_detection(self, v_ego, frogpilot_toggles):
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curve_detected = (1 / self.frogpilot_planner.road_curvature)**0.5 < v_ego
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curve_active = (0.9 / self.frogpilot_planner.road_curvature)**0.5 < v_ego and self.curve_detected
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self.curvature_mac.add_data(curve_detected or curve_active)
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self.curve_detected = self.curvature_mac.get_moving_average() >= PROBABILITY
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def slow_lead(self, tracking_lead, v_lead, frogpilot_toggles):
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if tracking_lead:
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slower_lead = self.frogpilot_planner.slower_lead and frogpilot_toggles.conditional_slower_lead
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stopped_lead = frogpilot_toggles.conditional_stopped_lead and v_lead < 1
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self.slow_lead_mac.add_data(slower_lead or stopped_lead)
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self.slow_lead_detected = self.slow_lead_mac.get_moving_average() >= PROBABILITY
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else:
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self.slow_lead_mac.reset_data()
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self.slow_lead_detected = False
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def stop_sign_and_light(self, tracking_lead, v_ego, frogpilot_toggles):
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model_projection = PLANNER_TIME - (5 if frogpilot_toggles.less_sensitive_lights else 3)
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model_stopping = self.frogpilot_planner.model_length < v_ego * model_projection
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self.stop_light_mac.add_data((self.frogpilot_planner.model_stopped or model_stopping) and not (self.curve_detected or tracking_lead))
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self.stop_light_detected = self.stop_light_mac.get_moving_average() >= PROBABILITY
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