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256 lines
10 KiB
256 lines
10 KiB
#!/usr/bin/env python3
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import os
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import math
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import json
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import numpy as np
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import cereal.messaging as messaging
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from cereal import car
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from cereal import log
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from common.params import Params, put_nonblocking
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from common.realtime import config_realtime_process, DT_MDL
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from common.numpy_fast import clip
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from selfdrive.locationd.models.car_kf import CarKalman, ObservationKind, States
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from selfdrive.locationd.models.constants import GENERATED_DIR
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from system.swaglog import cloudlog
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MAX_ANGLE_OFFSET_DELTA = 20 * DT_MDL # Max 20 deg/s
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ROLL_MAX_DELTA = math.radians(20.0) * DT_MDL # 20deg in 1 second is well within curvature limits
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ROLL_MIN, ROLL_MAX = math.radians(-10), math.radians(10)
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ROLL_LOWERED_MAX = math.radians(8)
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ROLL_STD_MAX = math.radians(1.5)
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LATERAL_ACC_SENSOR_THRESHOLD = 4.0
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OFFSET_MAX = 10.0
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OFFSET_LOWERED_MAX = 8.0
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class ParamsLearner:
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def __init__(self, CP, steer_ratio, stiffness_factor, angle_offset, P_initial=None):
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self.kf = CarKalman(GENERATED_DIR, steer_ratio, stiffness_factor, angle_offset, P_initial)
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self.kf.filter.set_global("mass", CP.mass)
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self.kf.filter.set_global("rotational_inertia", CP.rotationalInertia)
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self.kf.filter.set_global("center_to_front", CP.centerToFront)
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self.kf.filter.set_global("center_to_rear", CP.wheelbase - CP.centerToFront)
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self.kf.filter.set_global("stiffness_front", CP.tireStiffnessFront)
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self.kf.filter.set_global("stiffness_rear", CP.tireStiffnessRear)
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self.active = False
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self.speed = 0.0
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self.yaw_rate = 0.0
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self.yaw_rate_std = 0.0
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self.roll = 0.0
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self.steering_angle = 0.0
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self.roll_valid = False
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def handle_log(self, t, which, msg):
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if which == 'liveLocationKalman':
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self.yaw_rate = msg.angularVelocityCalibrated.value[2]
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self.yaw_rate_std = msg.angularVelocityCalibrated.std[2]
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localizer_roll = msg.orientationNED.value[0]
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localizer_roll_std = np.radians(1) if np.isnan(msg.orientationNED.std[0]) else msg.orientationNED.std[0]
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self.roll_valid = (localizer_roll_std < ROLL_STD_MAX) and (ROLL_MIN < localizer_roll < ROLL_MAX) and msg.sensorsOK
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if self.roll_valid:
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roll = localizer_roll
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# Experimentally found multiplier of 2 to be best trade-off between stability and accuracy or similar?
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roll_std = 2 * localizer_roll_std
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else:
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# This is done to bound the road roll estimate when localizer values are invalid
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roll = 0.0
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roll_std = np.radians(10.0)
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self.roll = clip(roll, self.roll - ROLL_MAX_DELTA, self.roll + ROLL_MAX_DELTA)
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yaw_rate_valid = msg.angularVelocityCalibrated.valid
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yaw_rate_valid = yaw_rate_valid and 0 < self.yaw_rate_std < 10 # rad/s
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yaw_rate_valid = yaw_rate_valid and abs(self.yaw_rate) < 1 # rad/s
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if self.active:
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if msg.posenetOK:
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if yaw_rate_valid:
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self.kf.predict_and_observe(t,
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ObservationKind.ROAD_FRAME_YAW_RATE,
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np.array([[-self.yaw_rate]]),
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np.array([np.atleast_2d(self.yaw_rate_std**2)]))
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self.kf.predict_and_observe(t,
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ObservationKind.ROAD_ROLL,
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np.array([[self.roll]]),
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np.array([np.atleast_2d(roll_std**2)]))
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self.kf.predict_and_observe(t, ObservationKind.ANGLE_OFFSET_FAST, np.array([[0]]))
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# We observe the current stiffness and steer ratio (with a high observation noise) to bound
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# the respective estimate STD. Otherwise the STDs keep increasing, causing rapid changes in the
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# states in longer routes (especially straight stretches).
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stiffness = float(self.kf.x[States.STIFFNESS].item())
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steer_ratio = float(self.kf.x[States.STEER_RATIO].item())
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self.kf.predict_and_observe(t, ObservationKind.STIFFNESS, np.array([[stiffness]]))
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self.kf.predict_and_observe(t, ObservationKind.STEER_RATIO, np.array([[steer_ratio]]))
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elif which == 'carState':
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self.steering_angle = msg.steeringAngleDeg
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self.speed = msg.vEgo
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in_linear_region = abs(self.steering_angle) < 45
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self.active = self.speed > 1 and in_linear_region
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if self.active:
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self.kf.predict_and_observe(t, ObservationKind.STEER_ANGLE, np.array([[math.radians(msg.steeringAngleDeg)]]))
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self.kf.predict_and_observe(t, ObservationKind.ROAD_FRAME_X_SPEED, np.array([[self.speed]]))
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if not self.active:
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# Reset time when stopped so uncertainty doesn't grow
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self.kf.filter.set_filter_time(t)
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self.kf.filter.reset_rewind()
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def check_valid_with_hysteresis(current_valid: bool, val: float, threshold: float, lowered_threshold: float):
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if current_valid:
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current_valid = abs(val) < threshold
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else:
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current_valid = abs(val) < lowered_threshold
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return current_valid
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def main(sm=None, pm=None):
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config_realtime_process([0, 1, 2, 3], 5)
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DEBUG = bool(int(os.getenv("DEBUG", "0")))
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REPLAY = bool(int(os.getenv("REPLAY", "0")))
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if sm is None:
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sm = messaging.SubMaster(['liveLocationKalman', 'carState'], poll=['liveLocationKalman'])
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if pm is None:
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pm = messaging.PubMaster(['liveParameters'])
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params_reader = Params()
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# wait for stats about the car to come in from controls
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cloudlog.info("paramsd is waiting for CarParams")
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with car.CarParams.from_bytes(params_reader.get("CarParams", block=True)) as msg:
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CP = msg
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cloudlog.info("paramsd got CarParams")
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min_sr, max_sr = 0.5 * CP.steerRatio, 2.0 * CP.steerRatio
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params = params_reader.get("LiveParameters")
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# Check if car model matches
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if params is not None:
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params = json.loads(params)
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if params.get('carFingerprint', None) != CP.carFingerprint:
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cloudlog.info("Parameter learner found parameters for wrong car.")
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params = None
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# Check if starting values are sane
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if params is not None:
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try:
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steer_ratio_sane = min_sr <= params['steerRatio'] <= max_sr
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if not steer_ratio_sane:
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cloudlog.info(f"Invalid starting values found {params}")
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params = None
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except Exception as e:
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cloudlog.info(f"Error reading params {params}: {str(e)}")
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params = None
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# TODO: cache the params with the capnp struct
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if params is None:
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params = {
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'carFingerprint': CP.carFingerprint,
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'steerRatio': CP.steerRatio,
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'stiffnessFactor': 1.0,
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'angleOffsetAverageDeg': 0.0,
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}
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cloudlog.info("Parameter learner resetting to default values")
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if not REPLAY:
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# When driving in wet conditions the stiffness can go down, and then be too low on the next drive
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# Without a way to detect this we have to reset the stiffness every drive
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params['stiffnessFactor'] = 1.0
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pInitial = None
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if DEBUG:
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pInitial = np.array(params['filterState']['std']) if 'filterState' in params else None
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learner = ParamsLearner(CP, params['steerRatio'], params['stiffnessFactor'], math.radians(params['angleOffsetAverageDeg']), pInitial)
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angle_offset_average = params['angleOffsetAverageDeg']
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angle_offset = angle_offset_average
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roll = 0.0
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avg_offset_valid = True
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total_offset_valid = True
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roll_valid = True
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while True:
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sm.update()
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if sm.all_checks():
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for which in sorted(sm.updated.keys(), key=lambda x: sm.logMonoTime[x]):
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if sm.updated[which]:
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t = sm.logMonoTime[which] * 1e-9
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learner.handle_log(t, which, sm[which])
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if sm.updated['liveLocationKalman']:
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x = learner.kf.x
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P = np.sqrt(learner.kf.P.diagonal())
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if not all(map(math.isfinite, x)):
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cloudlog.error("NaN in liveParameters estimate. Resetting to default values")
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learner = ParamsLearner(CP, CP.steerRatio, 1.0, 0.0)
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x = learner.kf.x
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angle_offset_average = clip(math.degrees(x[States.ANGLE_OFFSET].item()),
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angle_offset_average - MAX_ANGLE_OFFSET_DELTA, angle_offset_average + MAX_ANGLE_OFFSET_DELTA)
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angle_offset = clip(math.degrees(x[States.ANGLE_OFFSET].item() + x[States.ANGLE_OFFSET_FAST].item()),
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angle_offset - MAX_ANGLE_OFFSET_DELTA, angle_offset + MAX_ANGLE_OFFSET_DELTA)
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roll = clip(float(x[States.ROAD_ROLL].item()), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA)
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roll_std = float(P[States.ROAD_ROLL].item())
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# Account for the opposite signs of the yaw rates
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sensors_valid = bool(abs(learner.speed * (x[States.YAW_RATE].item() + learner.yaw_rate)) < LATERAL_ACC_SENSOR_THRESHOLD)
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avg_offset_valid = check_valid_with_hysteresis(avg_offset_valid, angle_offset_average, OFFSET_MAX, OFFSET_LOWERED_MAX)
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total_offset_valid = check_valid_with_hysteresis(total_offset_valid, angle_offset, OFFSET_MAX, OFFSET_LOWERED_MAX)
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roll_valid = check_valid_with_hysteresis(roll_valid, roll, ROLL_MAX, ROLL_LOWERED_MAX)
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msg = messaging.new_message('liveParameters')
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liveParameters = msg.liveParameters
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liveParameters.posenetValid = True
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liveParameters.sensorValid = sensors_valid
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liveParameters.steerRatio = float(x[States.STEER_RATIO].item())
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liveParameters.stiffnessFactor = float(x[States.STIFFNESS].item())
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liveParameters.roll = roll
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liveParameters.angleOffsetAverageDeg = angle_offset_average
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liveParameters.angleOffsetDeg = angle_offset
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liveParameters.valid = all((
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avg_offset_valid,
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total_offset_valid,
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roll_valid,
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roll_std < ROLL_STD_MAX,
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0.2 <= liveParameters.stiffnessFactor <= 5.0,
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min_sr <= liveParameters.steerRatio <= max_sr,
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))
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liveParameters.steerRatioStd = float(P[States.STEER_RATIO].item())
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liveParameters.stiffnessFactorStd = float(P[States.STIFFNESS].item())
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liveParameters.angleOffsetAverageStd = float(P[States.ANGLE_OFFSET].item())
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liveParameters.angleOffsetFastStd = float(P[States.ANGLE_OFFSET_FAST].item())
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if DEBUG:
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liveParameters.filterState = log.LiveLocationKalman.Measurement.new_message()
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liveParameters.filterState.value = x.tolist()
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liveParameters.filterState.std = P.tolist()
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liveParameters.filterState.valid = True
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msg.valid = sm.all_checks()
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if sm.frame % 1200 == 0: # once a minute
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params = {
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'carFingerprint': CP.carFingerprint,
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'steerRatio': liveParameters.steerRatio,
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'stiffnessFactor': liveParameters.stiffnessFactor,
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'angleOffsetAverageDeg': liveParameters.angleOffsetAverageDeg,
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}
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put_nonblocking("LiveParameters", json.dumps(params))
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pm.send('liveParameters', msg)
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if __name__ == "__main__":
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main()
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