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https://github.com/firestar5683/StarPilot.git
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firestar5683
107 lines
5.8 KiB
Python
107 lines
5.8 KiB
Python
#!/usr/bin/env python3
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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.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import COMFORT_BRAKE, desired_follow_distance, get_jerk_factor, get_T_FOLLOW
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from openpilot.frogpilot.common.frogpilot_variables import CITY_SPEED_LIMIT
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TRAFFIC_MODE_BP = [0., CITY_SPEED_LIMIT]
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PERSONALITY_BP = [20. * CV.KPH_TO_MS, 90. * CV.KPH_TO_MS]
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class FrogPilotFollowing:
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def __init__(self, FrogPilotPlanner):
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self.frogpilot_planner = FrogPilotPlanner
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self.disable_throttle = False
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self.following_lead = False
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self.slower_lead = False
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self.acceleration_jerk = 0
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self.danger_jerk = 0
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self.desired_follow_distance = 0
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self.speed_jerk = 0
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self.t_follow = 0
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def update(self, v_ego, sm, frogpilot_toggles):
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if sm["controlsState"].enabled and sm["frogpilotCarState"].trafficModeEnabled:
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if sm["carState"].aEgo >= 0:
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self.base_acceleration_jerk = float(np.interp(v_ego, TRAFFIC_MODE_BP, frogpilot_toggles.traffic_mode_jerk_acceleration))
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self.base_speed_jerk = float(np.interp(v_ego, TRAFFIC_MODE_BP, frogpilot_toggles.traffic_mode_jerk_speed))
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else:
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self.base_acceleration_jerk = float(np.interp(v_ego, TRAFFIC_MODE_BP, frogpilot_toggles.traffic_mode_jerk_deceleration))
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self.base_speed_jerk = float(np.interp(v_ego, TRAFFIC_MODE_BP, frogpilot_toggles.traffic_mode_jerk_speed_decrease))
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self.base_danger_jerk = float(np.interp(v_ego, TRAFFIC_MODE_BP, frogpilot_toggles.traffic_mode_jerk_danger))
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self.t_follow = float(np.interp(v_ego, TRAFFIC_MODE_BP, frogpilot_toggles.traffic_mode_follow))
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elif sm["controlsState"].enabled:
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if sm["carState"].aEgo >= 0:
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self.base_acceleration_jerk, self.base_danger_jerk, self.base_speed_jerk = get_jerk_factor(
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frogpilot_toggles.aggressive_jerk_acceleration, frogpilot_toggles.aggressive_jerk_danger, frogpilot_toggles.aggressive_jerk_speed,
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frogpilot_toggles.standard_jerk_acceleration, frogpilot_toggles.standard_jerk_danger, frogpilot_toggles.standard_jerk_speed,
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frogpilot_toggles.relaxed_jerk_acceleration, frogpilot_toggles.relaxed_jerk_danger, frogpilot_toggles.relaxed_jerk_speed,
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frogpilot_toggles.custom_personalities, sm["controlsState"].personality
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)
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else:
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self.base_acceleration_jerk, self.base_danger_jerk, self.base_speed_jerk = get_jerk_factor(
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frogpilot_toggles.aggressive_jerk_deceleration, frogpilot_toggles.aggressive_jerk_danger, frogpilot_toggles.aggressive_jerk_speed_decrease,
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frogpilot_toggles.standard_jerk_deceleration, frogpilot_toggles.standard_jerk_danger, frogpilot_toggles.standard_jerk_speed_decrease,
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frogpilot_toggles.relaxed_jerk_deceleration, frogpilot_toggles.relaxed_jerk_danger, frogpilot_toggles.relaxed_jerk_speed_decrease,
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frogpilot_toggles.custom_personalities, sm["controlsState"].personality
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)
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t_follow_param = get_T_FOLLOW(
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frogpilot_toggles.aggressive_follow,
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frogpilot_toggles.standard_follow,
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frogpilot_toggles.relaxed_follow,
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frogpilot_toggles.custom_personalities, sm["controlsState"].personality
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)
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if isinstance(t_follow_param, list):
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self.t_follow = float(np.interp(v_ego, PERSONALITY_BP, t_follow_param))
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else:
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self.t_follow = float(t_follow_param)
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else:
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self.base_acceleration_jerk = 0
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self.base_danger_jerk = 0
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self.base_speed_jerk = 0
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self.t_follow = 0
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self.acceleration_jerk = self.base_acceleration_jerk
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self.danger_jerk = self.base_danger_jerk
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self.speed_jerk = self.base_speed_jerk
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self.following_lead = self.frogpilot_planner.tracking_lead and self.frogpilot_planner.lead_one.dRel < (self.t_follow * 2) * v_ego
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self.disable_throttle = self.frogpilot_planner.tracking_lead and not self.following_lead
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self.disable_throttle &= self.frogpilot_planner.lead_one.dRel + 6.0 < (self.t_follow * 2 * 2) * v_ego
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self.disable_throttle &= self.frogpilot_planner.lead_one.vLead < v_ego * 0.75
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if sm["controlsState"].enabled and self.frogpilot_planner.tracking_lead:
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self.update_follow_values(self.frogpilot_planner.lead_one.dRel, v_ego, self.frogpilot_planner.lead_one.vLead, frogpilot_toggles)
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self.desired_follow_distance = int(desired_follow_distance(v_ego, self.frogpilot_planner.lead_one.vLead, self.t_follow))
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else:
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self.desired_follow_distance = 0
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def update_follow_values(self, lead_distance, v_ego, v_lead, frogpilot_toggles):
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# Offset by FrogAi for FrogPilot for a more natural approach to a faster lead
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if frogpilot_toggles.human_following and v_lead > v_ego:
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distance_factor = max(lead_distance - (v_ego * self.t_follow), 1)
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from frogpilot.common.frogpilot_variables import get_frogpilot_toggles
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fp_toggles = get_frogpilot_toggles()
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acceleration_offset = float(np.clip(fp_toggles.stop_distance - v_ego, 1, distance_factor))
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self.acceleration_jerk /= acceleration_offset
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self.speed_jerk /= acceleration_offset
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self.t_follow /= acceleration_offset
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# Offset by FrogAi for FrogPilot for a more natural approach to a slower lead
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if (frogpilot_toggles.conditional_slower_lead or frogpilot_toggles.human_following) and v_lead < v_ego:
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distance_factor = max(lead_distance - (v_lead * self.t_follow), 1)
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braking_offset = float(np.clip(min(v_ego - v_lead, v_lead) - COMFORT_BRAKE, 1, distance_factor))
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if frogpilot_toggles.human_following:
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from frogpilot.common.frogpilot_variables import get_frogpilot_toggles
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fp_toggles = get_frogpilot_toggles()
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far_lead_offset = max(lead_distance - (v_ego * self.t_follow) - fp_toggles.stop_distance, 0)
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self.t_follow /= braking_offset + far_lead_offset
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self.slower_lead = braking_offset > 1
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