carrot/selfdrive/controls/lib/latcontrol_torque.py

315 lines
17 KiB
Python

from collections import deque
import math
import numpy as np
from cereal import log
from openpilot.common.filter_simple import FirstOrderFilter
from openpilot.selfdrive.modeld.constants import ModelConstants
from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
from opendbc.car.interfaces import LatControlInputs
from openpilot.selfdrive.controls.lib.latcontrol import LatControl
from openpilot.common.pid import PIDController
from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
from openpilot.common.params import Params
# At higher speeds (25+mph) we can assume:
# Lateral acceleration achieved by a specific car correlates to
# torque applied to the steering rack. It does not correlate to
# wheel slip, or to speed.
# This controller applies torque to achieve desired lateral
# accelerations. To compensate for the low speed effects we
# use a LOW_SPEED_FACTOR in the error. Additionally, there is
# friction in the steering wheel that needs to be overcome to
# move it at all, this is compensated for too.
LOW_SPEED_X = [0, 10, 20, 30]
LOW_SPEED_Y = [15, 13, 10, 5]
LOW_SPEED_Y_NN = [12, 3, 1, 0]
LAT_PLAN_MIN_IDX = 5
def get_predicted_lateral_jerk(lat_accels, t_diffs):
# compute finite difference between subsequent model_data.acceleration.y values
# this is just two calls of np.diff followed by an element-wise division
lat_accel_diffs = np.diff(lat_accels)
lat_jerk = lat_accel_diffs / t_diffs
# return as python list
return lat_jerk.tolist()
def sign(x):
return 1.0 if x > 0.0 else (-1.0 if x < 0.0 else 0.0)
def get_lookahead_value(future_vals, current_val):
if len(future_vals) == 0:
return current_val
same_sign_vals = [v for v in future_vals if sign(v) == sign(current_val)]
# if any future val has opposite sign of current val, return 0
if len(same_sign_vals) < len(future_vals):
return 0.0
# otherwise return the value with minimum absolute value
min_val = min(same_sign_vals + [current_val], key=lambda x: abs(x))
return min_val
# At a given roll, if pitch magnitude increases, the
# gravitational acceleration component starts pointing
# in the longitudinal direction, decreasing the lateral
# acceleration component. Here we do the same thing
# to the roll value itself, then passed to nnff.
def roll_pitch_adjust(roll, pitch):
return roll * math.cos(pitch)
class LatControlTorque(LatControl):
def __init__(self, CP, CI):
super().__init__(CP, CI)
self.torque_params = CP.lateralTuning.torque.as_builder()
self.pid = PIDController(self.torque_params.kp, self.torque_params.ki,
k_f=self.torque_params.kf, pos_limit=self.steer_max, neg_limit=-self.steer_max)
self.torque_from_lateral_accel = CI.torque_from_lateral_accel()
self.use_steering_angle = self.torque_params.useSteeringAngle
self.steering_angle_deadzone_deg = self.torque_params.steeringAngleDeadzoneDeg
# carrot
self.frame = 0
self.params = Params()
self.lateralTorqueCustom = self.params.get_int("LateralTorqueCustom")
self.latAccelFactor_default = self.torque_params.latAccelFactor
self.latAccelOffset_default = self.torque_params.latAccelOffset
self.friction_default = self.torque_params.friction
self.dampingFactor = 0
self.error_last = 0.0
# Twilsonco's Lateral Neural Network Feedforward
self.use_nnff = CI.use_nnff
self.use_nnff_lite = CI.use_nnff_lite
if self.use_nnff or self.use_nnff_lite:
# Instantaneous lateral jerk changes very rapidly, making it not useful on its own,
# however, we can "look ahead" to the future planned lateral jerk in order to guage
# whether the current desired lateral jerk will persist into the future, i.e.
# whether it's "deliberate" or not. This lets us simply ignore short-lived jerk.
# Note that LAT_PLAN_MIN_IDX is defined above and is used in order to prevent
# using a "future" value that is actually planned to occur before the "current" desired
# value, which is offset by the steerActuatorDelay.
self.friction_look_ahead_v = [1.4, 2.0] # how many seconds in the future to look ahead in [0, ~2.1] in 0.1 increments
self.friction_look_ahead_bp = [9.0, 30.0] # corresponding speeds in m/s in [0, ~40] in 1.0 increments
# Scaling the lateral acceleration "friction response" could be helpful for some.
# Increase for a stronger response, decrease for a weaker response.
self.lat_jerk_friction_factor = 0.4
self.lat_accel_friction_factor = 0.7 # in [0, 3], in 0.05 increments. 3 is arbitrary safety limit
# precompute time differences between ModelConstants.T_IDXS
self.t_diffs = np.diff(ModelConstants.T_IDXS)
self.desired_lat_jerk_time = self.params.get_float("SteerActuatorDelay") * 0.01 + 0.3
if self.use_nnff:
self.pitch = FirstOrderFilter(0.0, 0.5, 0.01)
# NN model takes current v_ego, lateral_accel, lat accel/jerk error, roll, and past/future/planned data
# of lat accel and roll
# Past value is computed using previous desired lat accel and observed roll
self.torque_from_nn = CI.get_ff_nn
self.nn_friction_override = CI.lat_torque_nn_model.friction_override
# setup future time offsets
self.nn_time_offset = CP.steerActuatorDelay + 0.2
future_times = [0.3, 0.6, 1.0, 1.5] # seconds in the future
self.nn_future_times = [i + self.nn_time_offset for i in future_times]
self.nn_future_times_np = np.array(self.nn_future_times)
# setup past time offsets
self.past_times = [-0.3, -0.2, -0.1]
history_check_frames = [int(abs(i)*100) for i in self.past_times]
self.history_frame_offsets = [history_check_frames[0] - i for i in history_check_frames]
self.lateral_accel_desired_deque = deque(maxlen=history_check_frames[0])
self.roll_deque = deque(maxlen=history_check_frames[0])
self.error_deque = deque(maxlen=history_check_frames[0])
self.past_future_len = len(self.past_times) + len(self.nn_future_times)
def update_live_torque_params(self, latAccelFactor, latAccelOffset, friction):
if self.lateralTorqueCustom > 0:
return
self.torque_params.latAccelFactor = latAccelFactor
self.torque_params.latAccelOffset = latAccelOffset
self.torque_params.friction = friction
def update(self, active, CS, VM, params, steer_limited, desired_curvature, llk, model_data=None):
self.frame += 1
if self.frame % 10 == 0:
lateralTorqueCustom = self.params.get_int("LateralTorqueCustom")
self.dampingFactor = self.params.get_float("DampingFactor") * 0.01
if lateralTorqueCustom > 0:
self.torque_params.latAccelFactor = self.params.get_float("LateralTorqueAccelFactor")*0.001
self.torque_params.friction = self.params.get_float("LateralTorqueFriction")*0.001
lateralTorqueKp = self.params.get_float("LateralTorqueKpV")*0.01
lateralTorqueKi = self.params.get_float("LateralTorqueKiV")*0.01
lateralTorqueKf = self.params.get_float("LateralTorqueKf")*0.01
lateralTorqueKd = self.params.get_float("LateralTorqueKd")*0.01
self.pid._k_p = [[0], [lateralTorqueKp]]
self.pid._k_i = [[0], [lateralTorqueKi]]
self.pid.k_f = lateralTorqueKf
self.pid._k_d = [[0], [lateralTorqueKd]]
self.torque_params.latAccelOffset = self.latAccelOffset_default
elif self.lateralTorqueCustom > 1: # 1 -> 0, reset to default
self.torque_params.latAccelFactor = self.latAccelFactor_default
self.torque_params.friction = self.friction_default
self.torque_params.latAccelOffset = self.latAccelOffset_default
self.lateralTorqueCustom = lateralTorqueCustom
pid_log = log.ControlsState.LateralTorqueState.new_message()
nn_log = None
steeringRate = math.radians(CS.steeringRateDeg)
if not active:
output_torque = 0.0
pid_log.active = False
angle_steers_des = float(CS.steeringAngleDeg)
else:
angle_steers_des = math.degrees(VM.get_steer_from_curvature(-desired_curvature, CS.vEgo, params.roll))
angle_steers_des += params.angleOffsetDeg
actual_curvature_vm = -VM.calc_curvature(math.radians(CS.steeringAngleDeg - params.angleOffsetDeg), CS.vEgo, params.roll)
roll_compensation = params.roll * ACCELERATION_DUE_TO_GRAVITY
actual_lateral_jerk = 0.0
if self.use_steering_angle:
actual_curvature = actual_curvature_vm
curvature_deadzone = abs(VM.calc_curvature(math.radians(self.steering_angle_deadzone_deg), CS.vEgo, 0.0))
if self.use_nnff or self.use_nnff_lite:
actual_curvature_rate = -VM.calc_curvature(math.radians(CS.steeringRateDeg), CS.vEgo, 0.0)
actual_lateral_jerk = actual_curvature_rate * CS.vEgo ** 2
else:
actual_curvature_llk = llk.angularVelocityCalibrated.value[2] / CS.vEgo
actual_curvature = interp(CS.vEgo, [2.0, 5.0], [actual_curvature_vm, actual_curvature_llk])
curvature_deadzone = 0.0
desired_lateral_accel = desired_curvature * CS.vEgo ** 2
# desired rate is the desired rate of change in the setpoint, not the absolute desired curvature
# desired_lateral_jerk = desired_curvature_rate * CS.vEgo ** 2
actual_lateral_accel = actual_curvature * CS.vEgo ** 2
lateral_accel_deadzone = curvature_deadzone * CS.vEgo ** 2
low_speed_factor = np.interp(CS.vEgo, LOW_SPEED_X, LOW_SPEED_Y)**2
desired_lateral_accel = desired_curvature * CS.vEgo ** 2
setpoint = desired_lateral_accel + low_speed_factor * desired_curvature
measurement = actual_lateral_accel + low_speed_factor * actual_curvature
lateral_jerk_setpoint = 0
lateral_jerk_measurement = 0
lookahead_lateral_jerk = 0
model_good = model_data is not None and len(model_data.orientation.x) >= CONTROL_N
if model_good and (self.use_nnff or self.use_nnff_lite):
# prepare "look-ahead" desired lateral jerk
lookahead = np.interp(CS.vEgo, self.friction_look_ahead_bp, self.friction_look_ahead_v)
friction_upper_idx = next((i for i, val in enumerate(ModelConstants.T_IDXS) if val > lookahead), 16)
predicted_lateral_jerk = get_predicted_lateral_jerk(model_data.acceleration.y, self.t_diffs)
desired_lateral_jerk = (np.interp(self.desired_lat_jerk_time, ModelConstants.T_IDXS, model_data.acceleration.y) - desired_lateral_accel) / self.desired_lat_jerk_time
lookahead_lateral_jerk = get_lookahead_value(predicted_lateral_jerk[LAT_PLAN_MIN_IDX:friction_upper_idx], desired_lateral_jerk)
if self.use_steering_angle or lookahead_lateral_jerk == 0.0:
lookahead_lateral_jerk = 0.0
actual_lateral_jerk = 0.0
self.lat_accel_friction_factor = 1.0
lateral_jerk_setpoint = self.lat_jerk_friction_factor * lookahead_lateral_jerk
lateral_jerk_measurement = self.lat_jerk_friction_factor * actual_lateral_jerk
if self.use_nnff and model_good:
# update past data
pitch = 0
roll = params.roll
if len(llk.calibratedOrientationNED.value) > 1:
pitch = self.pitch.update(llk.calibratedOrientationNED.value[1])
roll = roll_pitch_adjust(roll, pitch)
self.roll_deque.append(roll)
self.lateral_accel_desired_deque.append(desired_lateral_accel)
# prepare past and future values
# adjust future times to account for longitudinal acceleration
adjusted_future_times = [t + 0.5*CS.aEgo*(t/max(CS.vEgo, 1.0)) for t in self.nn_future_times]
past_rolls = [self.roll_deque[min(len(self.roll_deque)-1, i)] for i in self.history_frame_offsets]
future_rolls = [roll_pitch_adjust(np.interp(t, ModelConstants.T_IDXS, model_data.orientation.x) + roll, np.interp(t, ModelConstants.T_IDXS, model_data.orientation.y) + pitch) for t in adjusted_future_times]
past_lateral_accels_desired = [self.lateral_accel_desired_deque[min(len(self.lateral_accel_desired_deque)-1, i)] for i in self.history_frame_offsets]
future_planned_lateral_accels = [np.interp(t, ModelConstants.T_IDXS, model_data.acceleration.y) for t in adjusted_future_times]
# compute NNFF error response
nnff_setpoint_input = [CS.vEgo, setpoint, lateral_jerk_setpoint, roll] \
+ [setpoint] * self.past_future_len \
+ past_rolls + future_rolls
# past lateral accel error shouldn't count, so use past desired like the setpoint input
nnff_measurement_input = [CS.vEgo, measurement, lateral_jerk_measurement, roll] \
+ [measurement] * self.past_future_len \
+ past_rolls + future_rolls
torque_from_setpoint = self.torque_from_nn(nnff_setpoint_input)
torque_from_measurement = self.torque_from_nn(nnff_measurement_input)
pid_log.error = float(torque_from_setpoint - torque_from_measurement)
error_blend_factor = np.interp(abs(desired_lateral_accel), [1.0, 2.0], [0.0, 1.0])
if error_blend_factor > 0.0: # blend in stronger error response when in high lat accel
nnff_error_input = [CS.vEgo, setpoint - measurement, lateral_jerk_setpoint - lateral_jerk_measurement, 0.0]
torque_from_error = self.torque_from_nn(nnff_error_input)
if sign(pid_log.error) == sign(torque_from_error) and abs(pid_log.error) < abs(torque_from_error):
pid_log.error = float(pid_log.error * (1.0 - error_blend_factor) + torque_from_error * error_blend_factor)
# compute feedforward (same as nn setpoint output)
error = setpoint - measurement
friction_input = self.lat_accel_friction_factor * error + self.lat_jerk_friction_factor * lookahead_lateral_jerk
nn_input = [CS.vEgo, desired_lateral_accel, friction_input, roll] \
+ past_lateral_accels_desired + future_planned_lateral_accels \
+ past_rolls + future_rolls
ff = self.torque_from_nn(nn_input)
# apply friction override for cars with low NN friction response
if self.nn_friction_override:
pid_log.error += self.torque_from_lateral_accel(LatControlInputs(0.0, 0.0, CS.vEgo, CS.aEgo), self.torque_params,
friction_input, lateral_accel_deadzone, friction_compensation=True, gravity_adjusted=False)
nn_log = nn_input + nnff_setpoint_input + nnff_measurement_input
else:
gravity_adjusted_lateral_accel = desired_lateral_accel - roll_compensation
torque_from_setpoint = self.torque_from_lateral_accel(LatControlInputs(setpoint, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
lateral_jerk_setpoint, lateral_accel_deadzone, friction_compensation=self.use_nnff_lite, gravity_adjusted=False)
torque_from_measurement = self.torque_from_lateral_accel(LatControlInputs(measurement, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
lateral_jerk_measurement, lateral_accel_deadzone, friction_compensation=self.use_nnff_lite, gravity_adjusted=False)
pid_log.error = float(torque_from_setpoint - torque_from_measurement)
error = desired_lateral_accel - actual_lateral_accel
if self.use_nnff_lite:
friction_input = self.lat_accel_friction_factor * error + self.lat_jerk_friction_factor * lookahead_lateral_jerk
else:
friction_input = error
ff = self.torque_from_lateral_accel(LatControlInputs(gravity_adjusted_lateral_accel, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
friction_input, lateral_accel_deadzone, friction_compensation=True,
gravity_adjusted=True)
freeze_integrator = steer_limited or CS.steeringPressed or CS.vEgo < 5
output_torque = self.pid.update(pid_log.error,
error_rate=pid_log.error - self.error_last,
feedforward=ff,
speed=CS.vEgo,
freeze_integrator=freeze_integrator)
damping_torque = - self.dampingFactor * steeringRate
output_torque += damping_torque
self.error_last = pid_log.error
pid_log.active = True
pid_log.p = float(self.pid.p)
pid_log.i = float(self.pid.i)
pid_log.d = float(self.pid.d)
pid_log.f = float(self.pid.f)
pid_log.output = float(-output_torque)
pid_log.actualLateralAccel = float(actual_lateral_accel)
pid_log.desiredLateralAccel = float(desired_lateral_accel)
pid_log.saturated = bool(self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS, steer_limited))
#if nn_log is not None:
# pid_log.nnLog = nn_log
# TODO left is positive in this convention
return -output_torque,angle_steers_des, pid_log