StarPilot/selfdrive/controls/lib/longitudinal_planner.py

#!/usr/bin/env python3
import math
import numpy as np
from openpilot.common.numpy_fast import clip, interp

import cereal.messaging as messaging
from openpilot.common.conversions import Conversions as CV
from openpilot.common.filter_simple import FirstOrderFilter
from openpilot.common.realtime import DT_MDL
from openpilot.selfdrive.modeld.constants import ModelConstants
from openpilot.selfdrive.car.interfaces import ACCEL_MIN, ACCEL_MAX
from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc
from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC
from openpilot.selfdrive.controls.lib.drive_helpers import V_CRUISE_UNSET, CONTROL_N, get_speed_error, get_accel_from_plan_tomb_raider
from openpilot.common.swaglog import cloudlog

LON_MPC_STEP = 0.2  # first step is 0.2s
A_CRUISE_MIN = -1.2
A_CRUISE_MAX_VALS = [1.6, 1.2, 0.8, 0.6]
A_CRUISE_MAX_BP = [0., 10.0, 25., 40.]
CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
ALLOW_THROTTLE_THRESHOLD = 0.5
MIN_ALLOW_THROTTLE_SPEED = 2.5

# Lookup table for turns
_A_TOTAL_MAX_V = [1.7, 3.2]
_A_TOTAL_MAX_BP = [20., 40.]


def get_max_accel(v_ego):
  return interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)

def get_coast_accel(pitch):
  return np.sin(pitch) * -5.65 - 0.3  # fitted from data using xx/projects/allow_throttle/compute_coast_accel.py


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
  """
  # FIXME: This function to calculate lateral accel is incorrect and should use the VehicleModel
  # The lookup table for turns should also be updated if we do this
  a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
  a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
  a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))

  return [a_target[0], min(a_target[1], a_x_allowed)]


def get_accel_from_plan_classic(CP, speeds, accels, vEgoStopping):
  if len(speeds) == CONTROL_N:
    v_target_now = interp(DT_MDL, CONTROL_N_T_IDX, speeds)
    a_target_now = interp(DT_MDL, CONTROL_N_T_IDX, accels)

    v_target = interp(CP.longitudinalActuatorDelay + DT_MDL, CONTROL_N_T_IDX, speeds)
    if v_target != v_target_now:
      a_target = 2 * (v_target - v_target_now) / CP.longitudinalActuatorDelay - a_target_now
    else:
      a_target = a_target_now

    v_target_1sec = interp(CP.longitudinalActuatorDelay + DT_MDL + 1.0, CONTROL_N_T_IDX, speeds)
  else:
    v_target = 0.0
    v_target_1sec = 0.0
    a_target = 0.0
  should_stop = (v_target < vEgoStopping and
                 v_target_1sec < vEgoStopping)
  return a_target, should_stop


def get_accel_from_plan(speeds, accels, action_t=DT_MDL, vEgoStopping=0.05):
  if len(speeds) == CONTROL_N:
    v_now = speeds[0]
    a_now = accels[0]

    v_target = interp(action_t, CONTROL_N_T_IDX, speeds)
    a_target = 2 * (v_target - v_now) / (action_t) - a_now
    v_target_1sec = interp(action_t + 1.0, CONTROL_N_T_IDX, speeds)
  else:
    v_target = 0.0
    v_target_1sec = 0.0
    a_target = 0.0
  should_stop = (v_target < vEgoStopping and
                 v_target_1sec < vEgoStopping)
  return a_target, should_stop


class LongitudinalPlanner:
  def __init__(self, CP, init_v=0.0, init_a=0.0, dt=DT_MDL):
    self.CP = CP
    self.mpc = LongitudinalMpc(dt=dt)
    self.fcw = False
    self.dt = dt
    self.allow_throttle = True
    self.mode = 'acc'

    self.generation = None

    self.a_desired = init_a
    self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
    self.v_model_error = 0.0

    self.v_desired_trajectory = np.zeros(CONTROL_N)
    self.a_desired_trajectory = np.zeros(CONTROL_N)
    self.j_desired_trajectory = np.zeros(CONTROL_N)
    self.solverExecutionTime = 0.0

  @property
  def mlsim(self):
      return self.generation in ("v8", "v10", "v11")

  def get_mpc_mode(self) -> str:
      """
      Determine the desired MPC mode: if not ML-SIM, MPC should follow self.mode;
      otherwise leave MPC.mode unchanged.
      """
      # For non-ML-SIM generations, MPC mode tracks self.mode
      if not self.mlsim:
          return self.mode
      # For ML-SIM (v8), preserve the existing MPC mode
      return getattr(self.mpc, 'mode', 'acc')

  @staticmethod
  def parse_model(model_msg, model_error, v_ego, taco_tune):
    if (len(model_msg.position.x) == ModelConstants.IDX_N and
      len(model_msg.velocity.x) == ModelConstants.IDX_N and
      len(model_msg.acceleration.x) == ModelConstants.IDX_N):
      x = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.position.x) - model_error * T_IDXS_MPC
      v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x) - model_error
      a = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.acceleration.x)
      j = np.zeros(len(T_IDXS_MPC))
    else:
      x = np.zeros(len(T_IDXS_MPC))
      v = np.zeros(len(T_IDXS_MPC))
      a = np.zeros(len(T_IDXS_MPC))
      j = np.zeros(len(T_IDXS_MPC))

    if taco_tune:
      max_lat_accel = interp(v_ego, [5, 10, 20], [1.5, 2.0, 3.0])
      curvatures = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.orientationRate.z) / np.clip(v, 0.3, 100.0)
      max_v = np.sqrt(max_lat_accel / (np.abs(curvatures) + 1e-3)) - 2.0
      v = np.minimum(max_v, v)

    if len(model_msg.meta.disengagePredictions.gasPressProbs) > 1:
      throttle_prob = model_msg.meta.disengagePredictions.gasPressProbs[1]
    else:
      throttle_prob = 1.0
    return x, v, a, j, throttle_prob

  def update(self, tinygrad_model, sm, frogpilot_toggles):
    self.generation = frogpilot_toggles.model_version
    if tinygrad_model:
      self.mpc.mode = 'acc'
      self.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
    else:
      self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
    if not self.mlsim:
      self.mpc.mode = self.mode

    if len(sm['carControl'].orientationNED) == 3:
      accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
    else:
      accel_coast = ACCEL_MAX

    v_ego = max(sm['carState'].vEgo, sm['carState'].vEgoCluster)
    v_cruise = sm['frogpilotPlan'].vCruise
    v_cruise_initialized = sm['controlsState'].vCruise != V_CRUISE_UNSET

    long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
    force_slow_decel = sm['controlsState'].forceDecel

    # Reset current state when not engaged, or user is controlling the speed
    reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['controlsState'].enabled
    # PCM cruise speed may be updated a few cycles later, check if initialized
    reset_state = reset_state or not v_cruise_initialized

    # No change cost when user is controlling the speed, or when standstill
    prev_accel_constraint = not (reset_state or sm['carState'].standstill)

    if self.mpc.mode == 'acc':
      accel_limits = [sm['frogpilotPlan'].minAcceleration, sm['frogpilotPlan'].maxAcceleration]
      steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
      accel_limits_turns = limit_accel_in_turns(v_ego, steer_angle_without_offset, accel_limits, self.CP)
    else:
      accel_limits = [ACCEL_MIN, ACCEL_MAX]
      accel_limits_turns = [ACCEL_MIN, ACCEL_MAX]

    if reset_state:
      self.v_desired_filter.x = v_ego
      # Clip aEgo to cruise limits to prevent large accelerations when becoming active
      self.a_desired = clip(sm['carState'].aEgo, accel_limits[0], accel_limits[1])

    # Prevent divergence, smooth in current v_ego
    self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
    # Compute model v_ego error
    self.v_model_error = get_speed_error(sm['modelV2'], v_ego)
    x, v, a, j, throttle_prob = self.parse_model(sm['modelV2'], self.v_model_error, v_ego, frogpilot_toggles.taco_tune)
    # Don't clip at low speeds since throttle_prob doesn't account for creep
    self.allow_throttle = throttle_prob > ALLOW_THROTTLE_THRESHOLD or v_ego <= MIN_ALLOW_THROTTLE_SPEED

    if not self.allow_throttle:
      clipped_accel_coast = max(accel_coast, accel_limits_turns[0])
      clipped_accel_coast_interp = interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [accel_limits_turns[1], clipped_accel_coast])
      accel_limits_turns[1] = min(accel_limits_turns[1], clipped_accel_coast_interp)

    if force_slow_decel:
      v_cruise = 0.0
    # clip limits, cannot init MPC outside of bounds
    accel_limits_turns[0] = min(accel_limits_turns[0], self.a_desired + 0.05)
    accel_limits_turns[1] = max(accel_limits_turns[1], self.a_desired - 0.05)

    self.lead_one = sm['radarState'].leadOne
    self.lead_two = sm['radarState'].leadTwo

    lead_dist = self.lead_one.dRel if self.lead_one.status else 50.0
    self.mpc.set_weights(sm['frogpilotPlan'].accelerationJerk, sm['frogpilotPlan'].dangerJerk, sm['frogpilotPlan'].speedJerk, prev_accel_constraint,
                         personality=sm['controlsState'].personality, v_ego=v_ego, lead_dist=lead_dist)
    self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
    self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
    # After deciding the MPC mode via get_mpc_mode(), ensure MPC uses that mode when not mlsim
    dec_mpc_mode = self.get_mpc_mode()
    if not self.mlsim:
      self.mpc.mode = dec_mpc_mode
    self.mpc.update(self.lead_one, self.lead_two, v_cruise, x, v, a, j, sm['frogpilotPlan'].tFollow,
                    sm['frogpilotPlan'].trackingLead, personality=sm['controlsState'].personality)

    self.a_desired_trajectory_full = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
    self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
    self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
    self.j_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC[:-1], self.mpc.j_solution)

    # TODO counter is only needed because radar is glitchy, remove once radar is gone
    self.fcw = self.mpc.crash_cnt > 2 and not sm['carState'].standstill
    if self.fcw:
      cloudlog.info("FCW triggered")

    # Safety checks for rubber-banding mitigation
    max_jerk = np.max(np.abs(self.mpc.j_solution))
    max_accel_change = np.max(np.abs(np.diff(self.mpc.a_solution)))
    if max_jerk > 5.0:  # m/s^3
      cloudlog.warning(f"High jerk detected: {max_jerk:.2f} m/s^3")
    if max_accel_change > 2.0:  # m/s^2
      cloudlog.warning(f"High acceleration change: {max_accel_change:.2f} m/s^2")

    # Interpolate 0.05 seconds and save as starting point for next iteration
    a_prev = self.a_desired
    self.a_desired = float(interp(self.dt, CONTROL_N_T_IDX, self.a_desired_trajectory))
    self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0

  def publish(self, classic_model, tinygrad_model, sm, pm, frogpilot_toggles):
    plan_send = messaging.new_message('longitudinalPlan')

    plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])

    longitudinalPlan = plan_send.longitudinalPlan
    longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
    longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
    longitudinalPlan.solverExecutionTime = self.mpc.solve_time

    longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
    longitudinalPlan.accels = self.a_desired_trajectory.tolist()
    longitudinalPlan.jerks = self.j_desired_trajectory.tolist()

    longitudinalPlan.hasLead = self.lead_one.status
    longitudinalPlan.longitudinalPlanSource = self.mpc.source
    longitudinalPlan.fcw = self.fcw

    if classic_model:
      a_target, should_stop = get_accel_from_plan_classic(self.CP, longitudinalPlan.speeds,
                                                          longitudinalPlan.accels, vEgoStopping=frogpilot_toggles.vEgoStopping)
    elif tinygrad_model:
      action_t =  self.CP.longitudinalActuatorDelay + DT_MDL
      output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan_tomb_raider(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
                                                                                    action_t=action_t, vEgoStopping=frogpilot_toggles.vEgoStopping)
      output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
      output_should_stop_e2e = sm['modelV2'].action.shouldStop

      # v9 uses a different longitudinal interface; keep MPC-only behavior even in blended mode
      if self.mode == 'acc' or self.generation == 'v9':
        a_target = output_a_target_mpc
        should_stop = output_should_stop_mpc
      else:
        a_target = min(output_a_target_mpc, output_a_target_e2e)
        should_stop = output_should_stop_e2e or output_should_stop_mpc
    else:
      action_t = self.CP.longitudinalActuatorDelay + DT_MDL
      a_target, should_stop = get_accel_from_plan(longitudinalPlan.speeds, longitudinalPlan.accels,
                                                  action_t=action_t, vEgoStopping=frogpilot_toggles.vEgoStopping)
    longitudinalPlan.aTarget = float(a_target)
    longitudinalPlan.shouldStop = bool(should_stop) or sm['frogpilotPlan'].forcingStopLength < 1
    longitudinalPlan.allowBrake = True
    longitudinalPlan.allowThrottle = self.allow_throttle

    pm.send('longitudinalPlan', plan_send)