openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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#!/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.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, MPC_COST_LONG, 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, 1.1, .8, .5, .3]
_A_CRUISE_MAX_V_FOLLOWING = [1.6, 1.6, 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(MPC_COST_LONG.TTC, MPC_COST_LONG.DISTANCE,
MPC_COST_LONG.ACCELERATION, MPC_COST_LONG.JERK)
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(MPC_COST_LONG.TTC, MPC_COST_LONG.DISTANCE,
MPC_COST_LONG.ACCELERATION, MPC_COST_LONG.JERK)
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