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132 lines
3.5 KiB
132 lines
3.5 KiB
#!/usr/bin/env python3
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# pylint: skip-file
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import numpy as np
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import math
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from tqdm import tqdm
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from typing import cast
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import seaborn as sns
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import matplotlib.pyplot as plt
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from selfdrive.car.honda.interface import CarInterface
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from selfdrive.car.honda.values import CAR
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from selfdrive.controls.lib.vehicle_model import VehicleModel, create_dyn_state_matrices
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from selfdrive.locationd.kalman.models.car_kf import CarKalman, ObservationKind, States
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T_SIM = 5 * 60 # s
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DT = 0.01
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CP = CarInterface.get_params(CAR.CIVIC)
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VM = VehicleModel(CP)
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x, y = 0, 0 # m, m
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psi = math.radians(0) # rad
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# The state is x = [v, r]^T
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# with v lateral speed [m/s], and r rotational speed [rad/s]
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state = np.array([[0.0], [0.0]])
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ts = np.arange(0, T_SIM, DT)
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speeds = 10 * np.sin(2 * np.pi * ts / 200.) + 25
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angle_offsets = math.radians(1.0) * np.ones_like(ts)
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angle_offsets[ts > 60] = 0
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steering_angles = cast(np.ndarray, np.radians(5 * np.cos(2 * np.pi * ts / 100.)))
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xs = []
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ys = []
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psis = []
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yaw_rates = []
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speed_ys = []
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kf_states = []
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kf_ps = []
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kf = CarKalman()
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for i, t in tqdm(list(enumerate(ts))):
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u = speeds[i]
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sa = steering_angles[i]
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ao = angle_offsets[i]
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A, B = create_dyn_state_matrices(u, VM)
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state += DT * (A.dot(state) + B.dot(sa + ao))
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x += u * math.cos(psi) * DT
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y += (float(state[0]) * math.sin(psi) + u * math.sin(psi)) * DT
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psi += float(state[1]) * DT
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kf.predict_and_observe(t, ObservationKind.CAL_DEVICE_FRAME_YAW_RATE, [float(state[1])])
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kf.predict_and_observe(t, ObservationKind.CAL_DEVICE_FRAME_XY_SPEED, [[u, float(state[0])]])
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kf.predict_and_observe(t, ObservationKind.STEER_ANGLE, [sa])
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kf.predict_and_observe(t, ObservationKind.ANGLE_OFFSET_FAST, [0])
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kf.predict(t)
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speed_ys.append(float(state[0]))
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yaw_rates.append(float(state[1]))
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kf_states.append(kf.x.copy())
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kf_ps.append(kf.P.copy())
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xs.append(x)
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ys.append(y)
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psis.append(psi)
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xs = np.asarray(xs)
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ys = np.asarray(ys)
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psis = np.asarray(psis)
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speed_ys = np.asarray(speed_ys)
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kf_states = np.asarray(kf_states)
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kf_ps = np.asarray(kf_ps)
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palette = sns.color_palette()
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def plot_with_bands(ts, state, label, ax, idx=1, converter=None):
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mean = kf_states[:, state].flatten()
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stds = np.sqrt(kf_ps[:, state, state].flatten())
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if converter is not None:
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mean = converter(mean)
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stds = converter(stds)
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sns.lineplot(ts, mean, label=label, ax=ax)
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ax.fill_between(ts, mean - stds, mean + stds, alpha=.2, color=palette[idx])
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print(kf.x)
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sns.set_context("paper")
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f, axes = plt.subplots(6, 1)
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sns.lineplot(ts, np.degrees(steering_angles), label='Steering Angle [deg]', ax=axes[0])
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plot_with_bands(ts, States.STEER_ANGLE, 'Steering Angle kf [deg]', axes[0], converter=np.degrees)
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sns.lineplot(ts, np.degrees(yaw_rates), label='Yaw Rate [deg]', ax=axes[1])
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plot_with_bands(ts, States.YAW_RATE, 'Yaw Rate kf [deg]', axes[1], converter=np.degrees)
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sns.lineplot(ts, np.ones_like(ts) * VM.sR, label='Steer ratio [-]', ax=axes[2])
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plot_with_bands(ts, States.STEER_RATIO, 'Steer ratio kf [-]', axes[2])
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axes[2].set_ylim([10, 20])
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sns.lineplot(ts, np.ones_like(ts), label='Tire stiffness[-]', ax=axes[3])
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plot_with_bands(ts, States.STIFFNESS, 'Tire stiffness kf [-]', axes[3])
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axes[3].set_ylim([0.8, 1.2])
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sns.lineplot(ts, np.degrees(angle_offsets), label='Angle offset [deg]', ax=axes[4])
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plot_with_bands(ts, States.ANGLE_OFFSET, 'Angle offset kf deg', axes[4], converter=np.degrees)
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plot_with_bands(ts, States.ANGLE_OFFSET_FAST, 'Fast Angle offset kf deg', axes[4], converter=np.degrees, idx=2)
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axes[4].set_ylim([-2, 2])
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sns.lineplot(ts, speeds, ax=axes[5])
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plt.show()
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