Release 260111
This commit is contained in:
Comma Device
304
selfdrive/controls/lib/latcontrol_torque.py
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304
selfdrive/controls/lib/latcontrol_torque.py
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from collections import deque
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import math
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import numpy as np
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from cereal import log
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
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from opendbc.car.interfaces import LatControlInputs
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from opendbc.car.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
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from openpilot.selfdrive.controls.lib.latcontrol import LatControl
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from openpilot.common.pid import PIDController
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from openpilot.common.params import Params
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# At higher speeds (25+mph) we can assume:
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# Lateral acceleration achieved by a specific car correlates to
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# torque applied to the steering rack. It does not correlate to
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# wheel slip, or to speed.
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# This controller applies torque to achieve desired lateral
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# accelerations. To compensate for the low speed effects we
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# use a LOW_SPEED_FACTOR in the error. Additionally, there is
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# friction in the steering wheel that needs to be overcome to
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# move it at all, this is compensated for too.
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LOW_SPEED_X = [0, 10, 20, 30]
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LOW_SPEED_Y = [15, 13, 10, 5]
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LOW_SPEED_Y_NN = [12, 3, 1, 0]
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LAT_PLAN_MIN_IDX = 5
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def get_predicted_lateral_jerk(lat_accels, t_diffs):
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# compute finite difference between subsequent model_data.acceleration.y values
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# this is just two calls of np.diff followed by an element-wise division
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lat_accel_diffs = np.diff(lat_accels)
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lat_jerk = lat_accel_diffs / t_diffs
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# return as python list
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return lat_jerk.tolist()
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def sign(x):
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return 1.0 if x > 0.0 else (-1.0 if x < 0.0 else 0.0)
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def get_lookahead_value(future_vals, current_val):
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if len(future_vals) == 0:
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return current_val
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same_sign_vals = [v for v in future_vals if sign(v) == sign(current_val)]
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# if any future val has opposite sign of current val, return 0
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if len(same_sign_vals) < len(future_vals):
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return 0.0
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# otherwise return the value with minimum absolute value
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min_val = min(same_sign_vals + [current_val], key=lambda x: abs(x))
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return min_val
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# At a given roll, if pitch magnitude increases, the
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# gravitational acceleration component starts pointing
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# in the longitudinal direction, decreasing the lateral
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# acceleration component. Here we do the same thing
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# to the roll value itself, then passed to nnff.
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def roll_pitch_adjust(roll, pitch):
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return roll * math.cos(pitch)
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class LatControlTorque(LatControl):
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def __init__(self, CP, CI):
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super().__init__(CP, CI)
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self.torque_params = CP.lateralTuning.torque.as_builder()
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self.pid = PIDController(self.torque_params.kp, self.torque_params.ki,
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k_f=self.torque_params.kf, pos_limit=self.steer_max, neg_limit=-self.steer_max)
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self.torque_from_lateral_accel = CI.torque_from_lateral_accel()
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self.use_steering_angle = self.torque_params.useSteeringAngle
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self.steering_angle_deadzone_deg = self.torque_params.steeringAngleDeadzoneDeg
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# carrot
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self.frame = 0
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self.params = Params()
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self.lateralTorqueCustom = self.params.get_int("LateralTorqueCustom")
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self.latAccelFactor_default = self.torque_params.latAccelFactor
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self.latAccelOffset_default = self.torque_params.latAccelOffset
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self.friction_default = self.torque_params.friction
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# Twilsonco's Lateral Neural Network Feedforward
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self.use_nnff = CI.use_nnff
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self.use_nnff_lite = CI.use_nnff_lite
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if self.use_nnff or self.use_nnff_lite:
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# Instantaneous lateral jerk changes very rapidly, making it not useful on its own,
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# however, we can "look ahead" to the future planned lateral jerk in order to guage
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# whether the current desired lateral jerk will persist into the future, i.e.
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# whether it's "deliberate" or not. This lets us simply ignore short-lived jerk.
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# Note that LAT_PLAN_MIN_IDX is defined above and is used in order to prevent
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# using a "future" value that is actually planned to occur before the "current" desired
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# value, which is offset by the steerActuatorDelay.
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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
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self.friction_look_ahead_bp = [9.0, 30.0] # corresponding speeds in m/s in [0, ~40] in 1.0 increments
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# Scaling the lateral acceleration "friction response" could be helpful for some.
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# Increase for a stronger response, decrease for a weaker response.
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self.lat_jerk_friction_factor = 0.4
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self.lat_accel_friction_factor = 0.7 # in [0, 3], in 0.05 increments. 3 is arbitrary safety limit
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# precompute time differences between ModelConstants.T_IDXS
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self.t_diffs = np.diff(ModelConstants.T_IDXS)
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self.desired_lat_jerk_time = self.params.get_float("SteerActuatorDelay") * 0.01 + 0.3
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if self.use_nnff:
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self.pitch = FirstOrderFilter(0.0, 0.5, 0.01)
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# NN model takes current v_ego, lateral_accel, lat accel/jerk error, roll, and past/future/planned data
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# of lat accel and roll
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# Past value is computed using previous desired lat accel and observed roll
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self.torque_from_nn = CI.get_ff_nn
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self.nn_friction_override = CI.lat_torque_nn_model.friction_override
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# setup future time offsets
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self.nn_time_offset = CP.steerActuatorDelay + 0.2
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future_times = [0.3, 0.6, 1.0, 1.5] # seconds in the future
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self.nn_future_times = [i + self.nn_time_offset for i in future_times]
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self.nn_future_times_np = np.array(self.nn_future_times)
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# setup past time offsets
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self.past_times = [-0.3, -0.2, -0.1]
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history_check_frames = [int(abs(i)*100) for i in self.past_times]
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self.history_frame_offsets = [history_check_frames[0] - i for i in history_check_frames]
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self.lateral_accel_desired_deque = deque(maxlen=history_check_frames[0])
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self.roll_deque = deque(maxlen=history_check_frames[0])
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self.error_deque = deque(maxlen=history_check_frames[0])
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self.past_future_len = len(self.past_times) + len(self.nn_future_times)
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def update_live_torque_params(self, latAccelFactor, latAccelOffset, friction):
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if self.lateralTorqueCustom > 0:
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return
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self.torque_params.latAccelFactor = latAccelFactor
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self.torque_params.latAccelOffset = latAccelOffset
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self.torque_params.friction = friction
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def update(self, active, CS, VM, params, steer_limited_by_controls, desired_curvature, CC, curvature_limited, model_data=None):
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self.frame += 1
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if self.frame % 100 == 0:
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lateralTorqueCustom = self.params.get_int("LateralTorqueCustom")
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if lateralTorqueCustom == 1:
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self.torque_params.latAccelFactor = self.params.get_float("LateralTorqueAccelFactor")*0.001
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self.torque_params.friction = self.params.get_float("LateralTorqueFriction")*0.001
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lateralTorqueKp = self.params.get_float("LateralTorqueKpV")*0.01
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lateralTorqueKi = self.params.get_float("LateralTorqueKiV")*0.01
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lateralTorqueKf = self.params.get_float("LateralTorqueKf")*0.01
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lateralTorqueKd = self.params.get_float("LateralTorqueKd")*0.01
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self.pid._k_p = [[0], [lateralTorqueKp]]
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self.pid._k_i = [[0], [lateralTorqueKi]]
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self.pid.k_f = lateralTorqueKf
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self.pid._k_d = [[0], [lateralTorqueKd]]
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self.torque_params.latAccelOffset = self.latAccelOffset_default
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elif self.lateralTorqueCustom > 1: # 1 -> 0, reset to default
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self.torque_params.latAccelFactor = self.latAccelFactor_default
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self.torque_params.friction = self.friction_default
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self.torque_params.latAccelOffset = self.latAccelOffset_default
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self.lateralTorqueCustom = lateralTorqueCustom
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pid_log = log.ControlsState.LateralTorqueState.new_message()
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nn_log = None
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if not active:
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output_torque = 0.0
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pid_log.active = False
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angle_steers_des = float(CS.steeringAngleDeg)
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else:
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angle_steers_des = math.degrees(VM.get_steer_from_curvature(-desired_curvature, CS.vEgo, params.roll))
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angle_steers_des += params.angleOffsetDeg
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actual_curvature_vm = -VM.calc_curvature(math.radians(CS.steeringAngleDeg - params.angleOffsetDeg), CS.vEgo, params.roll)
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roll_compensation = params.roll * ACCELERATION_DUE_TO_GRAVITY
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actual_lateral_jerk = 0.0
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if self.use_steering_angle:
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actual_curvature = actual_curvature_vm
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curvature_deadzone = abs(VM.calc_curvature(math.radians(self.steering_angle_deadzone_deg), CS.vEgo, 0.0))
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if self.use_nnff or self.use_nnff_lite:
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actual_curvature_rate = -VM.calc_curvature(math.radians(CS.steeringRateDeg), CS.vEgo, 0.0)
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actual_lateral_jerk = actual_curvature_rate * CS.vEgo ** 2
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else:
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actual_curvature_llk = CC.angularVelocity[2] / CS.vEgo #llk.angularVelocityCalibrated.value[2] / CS.vEgo
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actual_curvature = np.interp(CS.vEgo, [2.0, 5.0], [actual_curvature_vm, actual_curvature_llk])
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curvature_deadzone = 0.0
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desired_lateral_accel = desired_curvature * CS.vEgo ** 2
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# desired rate is the desired rate of change in the setpoint, not the absolute desired curvature
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# desired_lateral_jerk = desired_curvature_rate * CS.vEgo ** 2
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actual_lateral_accel = actual_curvature * CS.vEgo ** 2
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lateral_accel_deadzone = curvature_deadzone * CS.vEgo ** 2
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low_speed_factor = np.interp(CS.vEgo, LOW_SPEED_X, LOW_SPEED_Y)**2
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setpoint = desired_lateral_accel + low_speed_factor * desired_curvature
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measurement = actual_lateral_accel + low_speed_factor * actual_curvature
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lateral_jerk_setpoint = 0
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lateral_jerk_measurement = 0
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lookahead_lateral_jerk = 0
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model_good = model_data is not None and len(model_data.orientation.x) >= CONTROL_N
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if model_good and (self.use_nnff or self.use_nnff_lite):
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# prepare "look-ahead" desired lateral jerk
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lookahead = np.interp(CS.vEgo, self.friction_look_ahead_bp, self.friction_look_ahead_v)
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friction_upper_idx = next((i for i, val in enumerate(ModelConstants.T_IDXS) if val > lookahead), 16)
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predicted_lateral_jerk = get_predicted_lateral_jerk(model_data.acceleration.y, self.t_diffs)
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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
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lookahead_lateral_jerk = get_lookahead_value(predicted_lateral_jerk[LAT_PLAN_MIN_IDX:friction_upper_idx], desired_lateral_jerk)
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if self.use_steering_angle or lookahead_lateral_jerk == 0.0:
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lookahead_lateral_jerk = 0.0
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actual_lateral_jerk = 0.0
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self.lat_accel_friction_factor = 1.0
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lateral_jerk_setpoint = self.lat_jerk_friction_factor * lookahead_lateral_jerk
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lateral_jerk_measurement = self.lat_jerk_friction_factor * actual_lateral_jerk
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if self.use_nnff and model_good:
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# update past data
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pitch = 0
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roll = params.roll
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if len(CC.orientationNED) > 1:
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pitch = self.pitch.update(CC.orientationNED[1])
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roll = roll_pitch_adjust(roll, pitch)
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self.roll_deque.append(roll)
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self.lateral_accel_desired_deque.append(desired_lateral_accel)
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# prepare past and future values
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# adjust future times to account for longitudinal acceleration
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adjusted_future_times = [t + 0.5*CS.aEgo*(t/max(CS.vEgo, 1.0)) for t in self.nn_future_times]
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past_rolls = [self.roll_deque[min(len(self.roll_deque)-1, i)] for i in self.history_frame_offsets]
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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]
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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]
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future_planned_lateral_accels = [np.interp(t, ModelConstants.T_IDXS, model_data.acceleration.y) for t in adjusted_future_times]
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# compute NNFF error response
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nnff_setpoint_input = [CS.vEgo, setpoint, lateral_jerk_setpoint, roll] \
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+ [setpoint] * self.past_future_len \
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+ past_rolls + future_rolls
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# past lateral accel error shouldn't count, so use past desired like the setpoint input
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nnff_measurement_input = [CS.vEgo, measurement, lateral_jerk_measurement, roll] \
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+ [measurement] * self.past_future_len \
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+ past_rolls + future_rolls
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torque_from_setpoint = self.torque_from_nn(nnff_setpoint_input)
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torque_from_measurement = self.torque_from_nn(nnff_measurement_input)
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pid_log.error = float(torque_from_setpoint - torque_from_measurement)
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error_blend_factor = np.interp(abs(desired_lateral_accel), [1.0, 2.0], [0.0, 1.0])
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if error_blend_factor > 0.0: # blend in stronger error response when in high lat accel
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nnff_error_input = [CS.vEgo, setpoint - measurement, lateral_jerk_setpoint - lateral_jerk_measurement, 0.0]
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torque_from_error = self.torque_from_nn(nnff_error_input)
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if sign(pid_log.error) == sign(torque_from_error) and abs(pid_log.error) < abs(torque_from_error):
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pid_log.error = float(pid_log.error * (1.0 - error_blend_factor) + torque_from_error * error_blend_factor)
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# compute feedforward (same as nn setpoint output)
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error = setpoint - measurement
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friction_input = self.lat_accel_friction_factor * error + self.lat_jerk_friction_factor * lookahead_lateral_jerk
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nn_input = [CS.vEgo, desired_lateral_accel, friction_input, roll] \
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+ past_lateral_accels_desired + future_planned_lateral_accels \
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+ past_rolls + future_rolls
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ff = self.torque_from_nn(nn_input)
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# apply friction override for cars with low NN friction response
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if self.nn_friction_override:
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pid_log.error += self.torque_from_lateral_accel(LatControlInputs(0.0, 0.0, CS.vEgo, CS.aEgo), self.torque_params,
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friction_input, lateral_accel_deadzone, friction_compensation=True, gravity_adjusted=False)
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nn_log = nn_input + nnff_setpoint_input + nnff_measurement_input
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else:
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gravity_adjusted_lateral_accel = desired_lateral_accel - roll_compensation
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torque_from_setpoint = self.torque_from_lateral_accel(LatControlInputs(setpoint, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
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lateral_jerk_setpoint, lateral_accel_deadzone, friction_compensation=self.use_nnff_lite, gravity_adjusted=False)
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torque_from_measurement = self.torque_from_lateral_accel(LatControlInputs(measurement, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
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lateral_jerk_measurement, lateral_accel_deadzone, friction_compensation=self.use_nnff_lite, gravity_adjusted=False)
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pid_log.error = float(torque_from_setpoint - torque_from_measurement)
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error = desired_lateral_accel - actual_lateral_accel
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if self.use_nnff_lite:
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friction_input = self.lat_accel_friction_factor * error + self.lat_jerk_friction_factor * lookahead_lateral_jerk
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else:
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friction_input = error
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ff = self.torque_from_lateral_accel(LatControlInputs(gravity_adjusted_lateral_accel, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params,
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friction_input, lateral_accel_deadzone, friction_compensation=True,
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gravity_adjusted=True)
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freeze_integrator = steer_limited_by_controls or CS.steeringPressed or CS.vEgo < 5
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output_torque = self.pid.update(pid_log.error,
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feedforward=ff,
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speed=CS.vEgo,
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freeze_integrator=freeze_integrator)
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pid_log.active = True
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pid_log.p = float(self.pid.p)
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pid_log.i = float(self.pid.i)
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pid_log.d = float(self.pid.d)
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pid_log.f = float(self.pid.f)
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pid_log.output = float(-output_torque)
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pid_log.actualLateralAccel = float(actual_lateral_accel)
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pid_log.desiredLateralAccel = float(desired_lateral_accel)
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pid_log.saturated = bool(self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS, steer_limited_by_controls, curvature_limited))
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#if nn_log is not None:
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# pid_log.nnLog = nn_log
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# TODO left is positive in this convention
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return -output_torque,angle_steers_des, pid_log
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