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