#!/usr/bin/env python import os import zmq import numpy as np import math from collections import defaultdict from common.realtime import sec_since_boot from common.params import Params from common.numpy_fast import interp import selfdrive.messaging as messaging from selfdrive.swaglog import cloudlog from selfdrive.config import Conversions as CV from selfdrive.services import service_list from selfdrive.controls.lib.drive_helpers import create_event, EventTypes as ET from selfdrive.controls.lib.pathplanner import PathPlanner from selfdrive.controls.lib.longitudinal_mpc import libmpc_py from selfdrive.controls.lib.speed_smoother import speed_smoother from selfdrive.controls.lib.longcontrol import LongCtrlState, MIN_CAN_SPEED _DT = 0.01 # 100Hz _DT_MPC = 0.2 # 5Hz MAX_SPEED_ERROR = 2.0 AWARENESS_DECEL = -0.2 # car smoothly decel at .2m/s^2 when user is distracted _DEBUG = False _LEAD_ACCEL_TAU = 1.5 GPS_PLANNER_ADDR = "192.168.5.1" # lookup tables VS speed to determine min and max accels in cruise # make sure these accelerations are smaller than mpc limits _A_CRUISE_MIN_V = [-1.0, -.8, -.67, -.5, -.30] _A_CRUISE_MIN_BP = [ 0., 5., 10., 20., 40.] # need fast accel at very low speed for stop and go # make sure these accelerations are smaller than mpc limits _A_CRUISE_MAX_V = [1., 1., .8, .5, .3] _A_CRUISE_MAX_V_FOLLOWING = [1.5, 1.5, 1.2, .7, .3] _A_CRUISE_MAX_BP = [0., 5., 10., 20., 40.] # Lookup table for turns _A_TOTAL_MAX_V = [1.5, 1.9, 3.2] _A_TOTAL_MAX_BP = [0., 20., 40.] _FCW_A_ACT_V = [-3., -2.] _FCW_A_ACT_BP = [0., 30.] # max acceleration allowed in acc, which happens in restart A_ACC_MAX = max(_A_CRUISE_MAX_V_FOLLOWING) def calc_cruise_accel_limits(v_ego, following): a_cruise_min = interp(v_ego, _A_CRUISE_MIN_BP, _A_CRUISE_MIN_V) if following: a_cruise_max = interp(v_ego, _A_CRUISE_MAX_BP, _A_CRUISE_MAX_V_FOLLOWING) else: a_cruise_max = interp(v_ego, _A_CRUISE_MAX_BP, _A_CRUISE_MAX_V) return np.vstack([a_cruise_min, a_cruise_max]) def limit_accel_in_turns(v_ego, angle_steers, a_target, CP): """ This function returns a limited long acceleration allowed, depending on the existing lateral acceleration this should avoid accelerating when losing the target in turns """ deg_to_rad = np.pi / 180. # from can reading to rad a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V) a_y = v_ego**2 * angle_steers * deg_to_rad / (CP.steerRatio * CP.wheelbase) a_x_allowed = math.sqrt(max(a_total_max**2 - a_y**2, 0.)) a_target[1] = min(a_target[1], a_x_allowed) return a_target class FCWChecker(object): def __init__(self): self.reset_lead(0.0) def reset_lead(self, cur_time): self.last_fcw_a = 0.0 self.v_lead_max = 0.0 self.lead_seen_t = cur_time self.last_fcw_time = 0.0 self.last_min_a = 0.0 self.counters = defaultdict(lambda: 0) @staticmethod def calc_ttc(v_ego, a_ego, x_lead, v_lead, a_lead): max_ttc = 5.0 v_rel = v_ego - v_lead a_rel = a_ego - a_lead # assuming that closing gap ARel comes from lead vehicle decel, # then limit ARel so that v_lead will get to zero in no sooner than t_decel. # This helps underweighting ARel when v_lead is close to zero. t_decel = 2. a_rel = np.minimum(a_rel, v_lead/t_decel) # delta of the quadratic equation to solve for ttc delta = v_rel**2 + 2 * x_lead * a_rel # assign an arbitrary high ttc value if there is no solution to ttc if delta < 0.1 or (np.sqrt(delta) + v_rel < 0.1): ttc = max_ttc else: ttc = np.minimum(2 * x_lead / (np.sqrt(delta) + v_rel), max_ttc) return ttc def update(self, mpc_solution, cur_time, v_ego, a_ego, x_lead, v_lead, a_lead, y_lead, vlat_lead, fcw_lead, blinkers): mpc_solution_a = list(mpc_solution[0].a_ego) self.last_min_a = min(mpc_solution_a[1:]) self.v_lead_max = max(self.v_lead_max, v_lead) if (fcw_lead > 0.99): ttc = self.calc_ttc(v_ego, a_ego, x_lead, v_lead, a_lead) self.counters['v_ego'] = self.counters['v_ego'] + 1 if v_ego > 5.0 else 0 self.counters['ttc'] = self.counters['ttc'] + 1 if ttc < 2.5 else 0 self.counters['v_lead_max'] = self.counters['v_lead_max'] + 1 if self.v_lead_max > 2.5 else 0 self.counters['v_ego_lead'] = self.counters['v_ego_lead'] + 1 if v_ego > v_lead else 0 self.counters['lead_seen'] = self.counters['lead_seen'] + 0.33 self.counters['y_lead'] = self.counters['y_lead'] + 1 if abs(y_lead) < 1.0 else 0 self.counters['vlat_lead'] = self.counters['vlat_lead'] + 1 if abs(vlat_lead) < 0.4 else 0 self.counters['blinkers'] = self.counters['blinkers'] + 10.0 / (20 * 3.0) if not blinkers else 0 a_thr = interp(v_lead, _FCW_A_ACT_BP, _FCW_A_ACT_V) a_delta = min(mpc_solution_a[1:15]) - min(0.0, a_ego) fcw_allowed = all(c >= 10 for c in self.counters.values()) if (self.last_min_a < -3.0 or a_delta < a_thr) and fcw_allowed and self.last_fcw_time + 5.0 < cur_time: self.last_fcw_time = cur_time self.last_fcw_a = self.last_min_a return True return False class LongitudinalMpc(object): def __init__(self, mpc_id, live_longitudinal_mpc): self.live_longitudinal_mpc = live_longitudinal_mpc self.mpc_id = mpc_id self.setup_mpc() self.v_mpc = 0.0 self.v_mpc_future = 0.0 self.a_mpc = 0.0 self.v_cruise = 0.0 self.prev_lead_status = False self.prev_lead_x = 0.0 self.new_lead = False self.last_cloudlog_t = 0.0 def send_mpc_solution(self, qp_iterations, calculation_time): qp_iterations = max(0, qp_iterations) dat = messaging.new_message() dat.init('liveLongitudinalMpc') dat.liveLongitudinalMpc.xEgo = list(self.mpc_solution[0].x_ego) dat.liveLongitudinalMpc.vEgo = list(self.mpc_solution[0].v_ego) dat.liveLongitudinalMpc.aEgo = list(self.mpc_solution[0].a_ego) dat.liveLongitudinalMpc.xLead = list(self.mpc_solution[0].x_l) dat.liveLongitudinalMpc.vLead = list(self.mpc_solution[0].v_l) dat.liveLongitudinalMpc.aLead = list(self.mpc_solution[0].a_l) dat.liveLongitudinalMpc.aLeadTau = self.l dat.liveLongitudinalMpc.qpIterations = qp_iterations dat.liveLongitudinalMpc.mpcId = self.mpc_id dat.liveLongitudinalMpc.calculationTime = calculation_time self.live_longitudinal_mpc.send(dat.to_bytes()) def setup_mpc(self): ffi, self.libmpc = libmpc_py.get_libmpc(self.mpc_id) self.libmpc.init() self.mpc_solution = ffi.new("log_t *") self.cur_state = ffi.new("state_t *") self.cur_state[0].v_ego = 0 self.cur_state[0].a_ego = 0 self.l = _LEAD_ACCEL_TAU def set_cur_state(self, v, a): self.cur_state[0].v_ego = v self.cur_state[0].a_ego = a def update(self, CS, lead, v_cruise_setpoint): # Setup current mpc state self.cur_state[0].x_ego = 0.0 if lead is not None and lead.status: x_lead = lead.dRel v_lead = max(0.0, lead.vLead) a_lead = lead.aLeadK if (v_lead < 0.1 or -a_lead / 2.0 > v_lead): v_lead = 0.0 a_lead = 0.0 # Learn if constant acceleration if abs(a_lead) < 0.5: self.l = _LEAD_ACCEL_TAU else: self.l *= 0.9 l = max(self.l, -a_lead / (v_lead + 0.01)) self.new_lead = False if not self.prev_lead_status or abs(x_lead - self.prev_lead_x) > 2.5: self.libmpc.init_with_simulation(self.v_mpc, x_lead, v_lead, a_lead, l) self.new_lead = True self.prev_lead_status = True self.prev_lead_x = x_lead self.cur_state[0].x_l = x_lead self.cur_state[0].v_l = v_lead self.cur_state[0].a_l = a_lead else: self.prev_lead_status = False # Fake a fast lead car, so mpc keeps running self.cur_state[0].x_l = 50.0 self.cur_state[0].v_l = CS.vEgo + 10.0 self.cur_state[0].a_l = 0.0 l = _LEAD_ACCEL_TAU # Calculate mpc t = sec_since_boot() n_its = self.libmpc.run_mpc(self.cur_state, self.mpc_solution, l) duration = int((sec_since_boot() - t) * 1e9) self.send_mpc_solution(n_its, duration) # Get solution. MPC timestep is 0.2 s, so interpolation to 0.05 s is needed self.v_mpc = self.mpc_solution[0].v_ego[1] self.a_mpc = self.mpc_solution[0].a_ego[1] self.v_mpc_future = self.mpc_solution[0].v_ego[10] # Reset if NaN or goes through lead car dls = np.array(list(self.mpc_solution[0].x_l)[1:]) - np.array(list(self.mpc_solution[0].x_ego)[1:]) crashing = min(dls) < -50.0 nans = np.any(np.isnan(list(self.mpc_solution[0].v_ego))) backwards = min(list(self.mpc_solution[0].v_ego)[1:]) < -0.01 if ((backwards or crashing) and self.prev_lead_status) or nans: if t > self.last_cloudlog_t + 5.0: self.last_cloudlog_t = t cloudlog.warning("Longitudinal mpc %d reset - backwards: %s crashing: %s nan: %s" % ( self.mpc_id, backwards, crashing, nans)) self.libmpc.init() self.cur_state[0].v_ego = CS.vEgo self.cur_state[0].a_ego = 0.0 self.v_mpc = CS.vEgo self.a_mpc = CS.aEgo self.prev_lead_status = False class Planner(object): def __init__(self, CP, fcw_enabled): context = zmq.Context() self.CP = CP self.poller = zmq.Poller() self.live20 = messaging.sub_sock(context, service_list['live20'].port, conflate=True, poller=self.poller) self.model = messaging.sub_sock(context, service_list['model'].port, conflate=True, poller=self.poller) if os.environ.get('GPS_PLANNER_ACTIVE', False): self.gps_planner_plan = messaging.sub_sock(context, service_list['gpsPlannerPlan'].port, conflate=True, poller=self.poller, addr=GPS_PLANNER_ADDR) else: self.gps_planner_plan = None self.plan = messaging.pub_sock(context, service_list['plan'].port) self.live_longitudinal_mpc = messaging.pub_sock(context, service_list['liveLongitudinalMpc'].port) self.last_md_ts = 0 self.last_l20_ts = 0 self.last_model = 0. self.last_l20 = 0. self.model_dead = True self.radar_dead = True self.radar_errors = [] self.PP = PathPlanner() self.mpc1 = LongitudinalMpc(1, self.live_longitudinal_mpc) self.mpc2 = LongitudinalMpc(2, self.live_longitudinal_mpc) self.v_acc_start = 0.0 self.a_acc_start = 0.0 self.acc_start_time = sec_since_boot() self.v_acc = 0.0 self.v_acc_sol = 0.0 self.v_acc_future = 0.0 self.a_acc = 0.0 self.a_acc_sol = 0.0 self.v_cruise = 0.0 self.a_cruise = 0.0 self.lead_1 = None self.lead_2 = None self.longitudinalPlanSource = 'cruise' self.fcw = False self.fcw_checker = FCWChecker() self.fcw_enabled = fcw_enabled self.last_gps_planner_plan = None self.gps_planner_active = False def choose_solution(self, v_cruise_setpoint, enabled): if enabled: solutions = {'cruise': self.v_cruise} if self.mpc1.prev_lead_status: solutions['mpc1'] = self.mpc1.v_mpc if self.mpc2.prev_lead_status: solutions['mpc2'] = self.mpc2.v_mpc slowest = min(solutions, key=solutions.get) if _DEBUG: print "D_SOL", solutions, slowest, self.v_acc_sol, self.a_acc_sol print "D_V", self.mpc1.v_mpc, self.mpc2.v_mpc, self.v_cruise print "D_A", self.mpc1.a_mpc, self.mpc2.a_mpc, self.a_cruise self.longitudinalPlanSource = slowest # Choose lowest of MPC and cruise if slowest == 'mpc1': self.v_acc = self.mpc1.v_mpc self.a_acc = self.mpc1.a_mpc elif slowest == 'mpc2': self.v_acc = self.mpc2.v_mpc self.a_acc = self.mpc2.a_mpc elif slowest == 'cruise': self.v_acc = self.v_cruise self.a_acc = self.a_cruise self.v_acc_future = min([self.mpc1.v_mpc_future, self.mpc2.v_mpc_future, v_cruise_setpoint]) # this runs whenever we get a packet that can change the plan def update(self, CS, LoC, v_cruise_kph, user_distracted): cur_time = sec_since_boot() v_cruise_setpoint = v_cruise_kph * CV.KPH_TO_MS md = None l20 = None gps_planner_plan = None for socket, event in self.poller.poll(0): if socket is self.model: md = messaging.recv_one(socket) elif socket is self.live20: l20 = messaging.recv_one(socket) elif socket is self.gps_planner_plan: gps_planner_plan = messaging.recv_one(socket) if gps_planner_plan is not None: self.last_gps_planner_plan = gps_planner_plan if md is not None: self.last_md_ts = md.logMonoTime self.last_model = cur_time self.model_dead = False self.PP.update(CS.vEgo, md) if self.last_gps_planner_plan is not None: plan = self.last_gps_planner_plan.gpsPlannerPlan self.gps_planner_active = plan.valid if plan.valid: self.PP.d_poly = plan.poly self.PP.p_poly = plan.poly self.PP.c_poly = plan.poly self.PP.l_prob = 0.0 self.PP.r_prob = 0.0 self.PP.c_prob = 1.0 if l20 is not None: self.last_l20_ts = l20.logMonoTime self.last_l20 = cur_time self.radar_dead = False self.radar_errors = list(l20.live20.radarErrors) self.v_acc_start = self.v_acc_sol self.a_acc_start = self.a_acc_sol self.acc_start_time = cur_time self.lead_1 = l20.live20.leadOne self.lead_2 = l20.live20.leadTwo enabled = (LoC.long_control_state == LongCtrlState.pid) or (LoC.long_control_state == LongCtrlState.stopping) following = self.lead_1.status and self.lead_1.dRel < 45.0 and self.lead_1.vLeadK > CS.vEgo and self.lead_1.aLeadK > 0.0 # Calculate speed for normal cruise control if enabled: accel_limits = map(float, calc_cruise_accel_limits(CS.vEgo, following)) # TODO: make a separate lookup for jerk tuning jerk_limits = [min(-0.1, accel_limits[0]), max(0.1, accel_limits[1])] accel_limits = limit_accel_in_turns(CS.vEgo, CS.steeringAngle, accel_limits, self.CP) if user_distracted: # if user is not responsive to awareness alerts, then start a smooth deceleration accel_limits[1] = min(accel_limits[1], AWARENESS_DECEL) accel_limits[0] = min(accel_limits[0], accel_limits[1]) self.v_cruise, self.a_cruise = speed_smoother(self.v_acc_start, self.a_acc_start, v_cruise_setpoint, accel_limits[1], accel_limits[0], jerk_limits[1], jerk_limits[0], _DT_MPC) # cruise speed can't be negative even is user is distracted self.v_cruise = max(self.v_cruise, 0.) else: starting = LoC.long_control_state == LongCtrlState.starting a_ego = min(CS.aEgo, 0.0) reset_speed = MIN_CAN_SPEED if starting else CS.vEgo reset_accel = self.CP.startAccel if starting else a_ego self.v_acc = reset_speed self.a_acc = reset_accel self.v_acc_start = reset_speed self.a_acc_start = reset_accel self.v_cruise = reset_speed self.a_cruise = reset_accel self.v_acc_sol = reset_speed self.a_acc_sol = reset_accel self.mpc1.set_cur_state(self.v_acc_start, self.a_acc_start) self.mpc2.set_cur_state(self.v_acc_start, self.a_acc_start) self.mpc1.update(CS, self.lead_1, v_cruise_setpoint) self.mpc2.update(CS, self.lead_2, v_cruise_setpoint) self.choose_solution(v_cruise_setpoint, enabled) # determine fcw if self.mpc1.new_lead: self.fcw_checker.reset_lead(cur_time) blinkers = CS.leftBlinker or CS.rightBlinker self.fcw = self.fcw_checker.update(self.mpc1.mpc_solution, cur_time, CS.vEgo, CS.aEgo, self.lead_1.dRel, self.lead_1.vLead, self.lead_1.aLeadK, self.lead_1.yRel, self.lead_1.vLat, self.lead_1.fcw, blinkers) \ and not CS.brakePressed if self.fcw: cloudlog.info("FCW triggered %s", self.fcw_checker.counters) if cur_time - self.last_model > 0.5: self.model_dead = True if cur_time - self.last_l20 > 0.5: self.radar_dead = True # **** send the plan **** plan_send = messaging.new_message() plan_send.init('plan') events = [] if self.model_dead: events.append(create_event('modelCommIssue', [ET.NO_ENTRY, ET.IMMEDIATE_DISABLE])) if self.radar_dead or 'commIssue' in self.radar_errors: events.append(create_event('radarCommIssue', [ET.NO_ENTRY, ET.IMMEDIATE_DISABLE])) if 'fault' in self.radar_errors: events.append(create_event('radarFault', [ET.NO_ENTRY, ET.IMMEDIATE_DISABLE])) # Interpolation of trajectory dt = min(cur_time - self.acc_start_time, _DT_MPC + _DT) + _DT # no greater than dt mpc + dt, to prevent too high extraps self.a_acc_sol = self.a_acc_start + (dt / _DT_MPC) * (self.a_acc - self.a_acc_start) self.v_acc_sol = self.v_acc_start + dt * (self.a_acc_sol + self.a_acc_start) / 2.0 plan_send.plan.events = events plan_send.plan.mdMonoTime = self.last_md_ts plan_send.plan.l20MonoTime = self.last_l20_ts # lateral plan plan_send.plan.lateralValid = not self.model_dead plan_send.plan.dPoly = map(float, self.PP.d_poly) plan_send.plan.laneWidth = float(self.PP.lane_width) # longitudal plan plan_send.plan.longitudinalValid = not self.radar_dead plan_send.plan.vCruise = self.v_cruise plan_send.plan.aCruise = self.a_cruise plan_send.plan.vTarget = self.v_acc_sol plan_send.plan.aTarget = self.a_acc_sol plan_send.plan.vTargetFuture = self.v_acc_future plan_send.plan.hasLead = self.mpc1.prev_lead_status plan_send.plan.longitudinalPlanSource = self.longitudinalPlanSource plan_send.plan.gpsPlannerActive = self.gps_planner_active # Send out fcw fcw = self.fcw and (self.fcw_enabled or LoC.long_control_state != LongCtrlState.off) plan_send.plan.fcw = fcw self.plan.send(plan_send.to_bytes()) return plan_send