254 lines
10 KiB
Python
Executable File
254 lines
10 KiB
Python
Executable File
#!/usr/bin/env python3
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import math
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import numpy as np
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from openpilot.common.numpy_fast import clip, interp
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import cereal.messaging as messaging
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from openpilot.common.conversions import Conversions as CV
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.common.simple_kalman import KF1D
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from openpilot.common.realtime import DT_MDL
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from openpilot.common.swaglog import cloudlog
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.car.interfaces import ACCEL_MIN, ACCEL_MAX
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from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC, LEAD_ACCEL_TAU
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from openpilot.selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, CONTROL_N, get_speed_error
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LON_MPC_STEP = 0.2 # first step is 0.2s
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A_CRUISE_MIN = -1.2
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A_CRUISE_MAX_VALS = [1.6, 1.2, 0.8, 0.6]
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A_CRUISE_MAX_BP = [0., 10.0, 25., 40.]
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# Lookup table for turns
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_A_TOTAL_MAX_V = [1.7, 3.2]
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_A_TOTAL_MAX_BP = [20., 40.]
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# Kalman filter states enum
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LEAD_KALMAN_SPEED, LEAD_KALMAN_ACCEL = 0, 1
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def get_max_accel(v_ego):
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return interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
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def limit_accel_in_turns(v_ego, angle_steers, a_target, CP):
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"""
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This function returns a limited long acceleration allowed, depending on the existing lateral acceleration
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this should avoid accelerating when losing the target in turns
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"""
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# FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel
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# The lookup table for turns should also be updated if we do this
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a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
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a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
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a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))
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return [a_target[0], min(a_target[1], a_x_allowed)]
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def lead_kf(v_lead: float, dt: float = 0.05):
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# Lead Kalman Filter params, calculating K from A, C, Q, R requires the control library.
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# hardcoding a lookup table to compute K for values of radar_ts between 0.01s and 0.2s
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assert dt > .01 and dt < .2, "Radar time step must be between .01s and 0.2s"
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A = [[1.0, dt], [0.0, 1.0]]
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C = [1.0, 0.0]
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#Q = np.matrix([[10., 0.0], [0.0, 100.]])
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#R = 1e3
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#K = np.matrix([[ 0.05705578], [ 0.03073241]])
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dts = [dt * 0.01 for dt in range(1, 21)]
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K0 = [0.12287673, 0.14556536, 0.16522756, 0.18281627, 0.1988689, 0.21372394,
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0.22761098, 0.24069424, 0.253096, 0.26491023, 0.27621103, 0.28705801,
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0.29750003, 0.30757767, 0.31732515, 0.32677158, 0.33594201, 0.34485814,
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0.35353899, 0.36200124]
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K1 = [0.29666309, 0.29330885, 0.29042818, 0.28787125, 0.28555364, 0.28342219,
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0.28144091, 0.27958406, 0.27783249, 0.27617149, 0.27458948, 0.27307714,
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0.27162685, 0.27023228, 0.26888809, 0.26758976, 0.26633338, 0.26511557,
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0.26393339, 0.26278425]
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K = [[interp(dt, dts, K0)], [interp(dt, dts, K1)]]
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kf = KF1D([[v_lead], [0.0]], A, C, K)
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return kf
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class Lead:
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def __init__(self):
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self.dRel = 0.0
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self.yRel = 0.0
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self.vLead = 0.0
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self.aLead = 0.0
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self.vLeadK = 0.0
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self.aLeadK = 0.0
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self.aLeadTau = LEAD_ACCEL_TAU
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self.prob = 0.0
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self.status = False
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self.kf: KF1D | None = None
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def reset(self):
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self.status = False
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self.kf = None
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self.aLeadTau = LEAD_ACCEL_TAU
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def update(self, dRel: float, yRel: float, vLead: float, aLead: float, prob: float):
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self.dRel = dRel
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self.yRel = yRel
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self.vLead = vLead
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self.aLead = aLead
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self.prob = prob
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self.status = True
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if self.kf is None:
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self.kf = lead_kf(self.vLead)
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else:
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self.kf.update(self.vLead)
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self.vLeadK = float(self.kf.x[LEAD_KALMAN_SPEED][0])
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self.aLeadK = float(self.kf.x[LEAD_KALMAN_ACCEL][0])
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# Learn if constant acceleration
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if abs(self.aLeadK) < 0.5:
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self.aLeadTau = LEAD_ACCEL_TAU
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else:
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self.aLeadTau *= 0.9
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class LongitudinalPlanner:
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def __init__(self, CP, init_v=0.0, init_a=0.0, dt=DT_MDL):
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self.CP = CP
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self.mpc = LongitudinalMpc()
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self.fcw = False
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self.dt = dt
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self.a_desired = init_a
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self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
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self.v_model_error = 0.0
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self.lead_one = Lead()
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self.lead_two = Lead()
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self.v_desired_trajectory = np.zeros(CONTROL_N)
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self.a_desired_trajectory = np.zeros(CONTROL_N)
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self.j_desired_trajectory = np.zeros(CONTROL_N)
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self.solverExecutionTime = 0.0
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@staticmethod
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def parse_model(model_msg, model_error, v_ego, taco_tune):
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if (len(model_msg.position.x) == 33 and
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len(model_msg.velocity.x) == 33 and
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len(model_msg.acceleration.x) == 33):
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x = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC
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v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x) - model_error
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a = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.acceleration.x)
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j = np.zeros(len(T_IDXS_MPC))
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else:
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x = np.zeros(len(T_IDXS_MPC))
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v = np.zeros(len(T_IDXS_MPC))
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a = np.zeros(len(T_IDXS_MPC))
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j = np.zeros(len(T_IDXS_MPC))
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if taco_tune:
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max_lat_accel = interp(v_ego, [5, 10, 20], [1.5, 2.0, 3.0])
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curvatures = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.orientationRate.z) / np.clip(v, 0.3, 100.0)
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max_v = np.sqrt(max_lat_accel / (np.abs(curvatures) + 1e-3)) - 2.0
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v = np.minimum(max_v, v)
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return x, v, a, j
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def update(self, sm, frogpilot_toggles):
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self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
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v_ego = sm['carState'].vEgo
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v_cruise_kph = min(sm['controlsState'].vCruise, V_CRUISE_MAX)
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v_cruise = v_cruise_kph * CV.KPH_TO_MS
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long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
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force_slow_decel = sm['controlsState'].forceDecel
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# Reset current state when not engaged, or user is controlling the speed
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reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['controlsState'].enabled
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# No change cost when user is controlling the speed, or when standstill
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prev_accel_constraint = not (reset_state or sm['carState'].standstill)
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accel_limits = [sm['frogpilotPlan'].minAcceleration, sm['frogpilotPlan'].maxAcceleration]
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if self.mpc.mode == 'acc':
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accel_limits_turns = limit_accel_in_turns(v_ego, sm['carState'].steeringAngleDeg, accel_limits, self.CP)
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else:
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accel_limits_turns = [ACCEL_MIN, ACCEL_MAX]
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if reset_state:
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self.v_desired_filter.x = v_ego
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# Clip aEgo to cruise limits to prevent large accelerations when becoming active
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self.a_desired = clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])
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# Prevent divergence, smooth in current v_ego
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self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
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# Compute model v_ego error
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self.v_model_error = get_speed_error(sm['modelV2'], v_ego)
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if force_slow_decel:
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v_cruise = 0.0
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# clip limits, cannot init MPC outside of bounds
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accel_limits_turns[0] = min(accel_limits_turns[0], self.a_desired + 0.05)
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accel_limits_turns[1] = max(accel_limits_turns[1], self.a_desired - 0.05)
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if frogpilot_toggles.radarless_model:
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model_leads = list(sm['modelV2'].leadsV3)
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# TODO lead state should be invalidated if its different point than the previous one
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lead_states = [self.lead_one, self.lead_two]
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for index in range(len(lead_states)):
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if len(model_leads) > index:
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model_lead = model_leads[index]
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lead_states[index].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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lead_states[index].reset()
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else:
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self.lead_one = sm['radarState'].leadOne
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self.lead_two = sm['radarState'].leadTwo
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self.mpc.set_weights(sm['frogpilotPlan'].accelerationJerk, sm['frogpilotPlan'].speedJerk, prev_accel_constraint, personality=sm['controlsState'].personality)
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self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
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self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
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x, v, a, j = self.parse_model(sm['modelV2'], self.v_model_error, v_ego, frogpilot_toggles.taco_tune)
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self.mpc.update(self.lead_one, self.lead_two, sm['frogpilotPlan'].vCruise, x, v, a, j, sm['frogpilotPlan'].tFollow,
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sm['frogpilotCarControl'].trafficModeActive, frogpilot_toggles, personality=sm['controlsState'].personality)
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self.v_desired_trajectory_full = np.interp(ModelConstants.T_IDXS, T_IDXS_MPC, self.mpc.v_solution)
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self.a_desired_trajectory_full = np.interp(ModelConstants.T_IDXS, T_IDXS_MPC, self.mpc.a_solution)
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self.v_desired_trajectory = self.v_desired_trajectory_full[:CONTROL_N]
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self.a_desired_trajectory = self.a_desired_trajectory_full[:CONTROL_N]
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self.j_desired_trajectory = np.interp(ModelConstants.T_IDXS[:CONTROL_N], T_IDXS_MPC[:-1], self.mpc.j_solution)
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# TODO counter is only needed because radar is glitchy, remove once radar is gone
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self.fcw = self.mpc.crash_cnt > 2 and not sm['carState'].standstill
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if self.fcw:
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cloudlog.info("FCW triggered")
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# Interpolate 0.05 seconds and save as starting point for next iteration
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a_prev = self.a_desired
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self.a_desired = float(interp(self.dt, ModelConstants.T_IDXS[:CONTROL_N], self.a_desired_trajectory))
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self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0
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def publish(self, sm, pm):
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plan_send = messaging.new_message('longitudinalPlan')
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plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
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longitudinalPlan = plan_send.longitudinalPlan
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longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
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longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
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longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
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longitudinalPlan.accels = self.a_desired_trajectory.tolist()
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longitudinalPlan.jerks = self.j_desired_trajectory.tolist()
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longitudinalPlan.hasLead = self.lead_one.status
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longitudinalPlan.longitudinalPlanSource = self.mpc.source
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longitudinalPlan.fcw = self.fcw
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longitudinalPlan.solverExecutionTime = self.mpc.solve_time
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pm.send('longitudinalPlan', plan_send)
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