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162 lines
6.1 KiB
162 lines
6.1 KiB
#!/usr/bin/env python3
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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 common.params import Params, put_nonblocking
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from selfdrive.locationd.kalman.models.car_kf import (CarKalman,
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ObservationKind, States)
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from selfdrive.swaglog import cloudlog
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CARSTATE_DECIMATION = 5
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class ParamsLearner:
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def __init__(self, CP, steer_ratio, stiffness_factor, angle_offset):
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self.kf = CarKalman(steer_ratio, stiffness_factor, angle_offset)
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self.kf.filter.set_mass(CP.mass) # pylint: disable=no-member
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self.kf.filter.set_rotational_inertia(CP.rotationalInertia) # pylint: disable=no-member
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self.kf.filter.set_center_to_front(CP.centerToFront) # pylint: disable=no-member
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self.kf.filter.set_center_to_rear(CP.wheelbase - CP.centerToFront) # pylint: disable=no-member
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self.kf.filter.set_stiffness_front(CP.tireStiffnessFront) # pylint: disable=no-member
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self.kf.filter.set_stiffness_rear(CP.tireStiffnessRear) # pylint: disable=no-member
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self.active = False
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self.speed = 0
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self.steering_pressed = False
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self.steering_angle = 0
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self.carstate_counter = 0
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def handle_log(self, t, which, msg):
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if which == 'liveLocationKalman':
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v_calibrated = msg.velocityCalibrated.value
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v_calibrated_std = msg.velocityCalibrated.std
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yaw_rate = msg.angularVelocityCalibrated.value[2]
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yaw_rate_std = msg.angularVelocityCalibrated.std[2]
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self.active = v_calibrated[0] > 5
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in_linear_region = abs(self.steering_angle) < 45 or not self.steering_pressed
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if self.active and in_linear_region:
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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([[[-yaw_rate]]]),
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np.array([np.atleast_2d(yaw_rate_std**2)]))
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self.kf.predict_and_observe(t,
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ObservationKind.ROAD_FRAME_XY_SPEED,
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np.array([[[v_calibrated[0], -v_calibrated[1]]]]),
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np.array([np.diag([v_calibrated_std[0]**2, 1e6*v_calibrated_std[1]**2])]))
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self.kf.predict_and_observe(t, ObservationKind.ANGLE_OFFSET_FAST, np.array([[[0]]]))
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# Clamp values
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# x = self.kf.x
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# if not (10 < x[States.STEER_RATIO] < 25):
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# self.kf.predict_and_observe(t, ObservationKind.STEER_RATIO, np.array([[[15.0]]]))
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# if not (0.5 < x[States.STIFFNESS] < 3.0):
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# self.kf.predict_and_observe(t, ObservationKind.STIFFNESS, np.array([[[1.0]]]))
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elif which == 'carState':
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self.carstate_counter += 1
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if self.carstate_counter % CARSTATE_DECIMATION == 0:
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self.steering_angle = msg.steeringAngle
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self.steering_pressed = msg.steeringPressed
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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.steeringAngle)]]]))
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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.filter_time = t
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self.kf.filter.reset_rewind()
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def main(sm=None, pm=None):
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if sm is None:
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sm = messaging.SubMaster(['liveLocationKalman', 'carState'])
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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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CP = car.CarParams.from_bytes(params_reader.get("CarParams", block=True))
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cloudlog.info("paramsd got CarParams")
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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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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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'angleOffsetAverage': 0.0,
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}
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cloudlog.info("Parameter learner resetting to default values")
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learner = ParamsLearner(CP, params['steerRatio'], params['stiffnessFactor'], math.radians(params['angleOffsetAverage']))
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i = 0
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while True:
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sm.update()
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for which, updated in sm.updated.items():
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if not updated:
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continue
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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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# TODO: set valid to false when locationd stops sending
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# TODO: make sure controlsd knows when there is no gyro
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if sm.updated['carState']:
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msg = messaging.new_message('liveParameters')
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msg.logMonoTime = sm.logMonoTime['carState']
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msg.liveParameters.valid = True # TODO: Check if learned values are sane
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msg.liveParameters.posenetValid = True
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msg.liveParameters.sensorValid = True
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x = learner.kf.x
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msg.liveParameters.steerRatio = float(x[States.STEER_RATIO])
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msg.liveParameters.stiffnessFactor = float(x[States.STIFFNESS])
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msg.liveParameters.angleOffsetAverage = math.degrees(x[States.ANGLE_OFFSET])
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msg.liveParameters.angleOffset = msg.liveParameters.angleOffsetAverage + math.degrees(x[States.ANGLE_OFFSET_FAST])
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i += 1
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if i % 6000 == 0: # once a minute
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params = {
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'carFingerprint': CP.carFingerprint,
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'steerRatio': msg.liveParameters.steerRatio,
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'stiffnessFactor': msg.liveParameters.stiffnessFactor,
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'angleOffsetAverage': msg.liveParameters.angleOffsetAverage,
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}
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put_nonblocking("LiveParameters", json.dumps(params))
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# P = learner.kf.P
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# print()
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# print("sR", float(x[States.STEER_RATIO]), float(P[States.STEER_RATIO, States.STEER_RATIO])**0.5)
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# print("x ", float(x[States.STIFFNESS]), float(P[States.STIFFNESS, States.STIFFNESS])**0.5)
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# print("ao avg ", math.degrees(x[States.ANGLE_OFFSET]), math.degrees(P[States.ANGLE_OFFSET, States.ANGLE_OFFSET])**0.5)
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# print("ao ", math.degrees(x[States.ANGLE_OFFSET_FAST]), math.degrees(P[States.ANGLE_OFFSET_FAST, States.ANGLE_OFFSET_FAST])**0.5)
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pm.send('liveParameters', msg)
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if __name__ == "__main__":
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main()
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