openpilot0/selfdrive/debug/toyota_eps_factor.py

63 lines
1.6 KiB
Python
Executable File

#!/usr/bin/env python3
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from opendbc.car.toyota.values import STEER_THRESHOLD
from openpilot.tools.lib.logreader import LogReader
MIN_SAMPLES = 30 * 100
def to_signed(n, bits):
if n >= (1 << max((bits - 1), 0)):
n = n - (1 << max(bits, 0))
return n
def get_eps_factor(lr, plot=False):
engaged = False
steering_pressed = False
torque_cmd, eps_torque = None, None
cmds, eps = [], []
for msg in lr:
if msg.which() != 'can':
continue
for m in msg.can:
if m.address == 0x2e4 and m.src == 128:
engaged = bool(m.dat[0] & 1)
torque_cmd = to_signed((m.dat[1] << 8) | m.dat[2], 16)
elif m.address == 0x260 and m.src == 0:
eps_torque = to_signed((m.dat[5] << 8) | m.dat[6], 16)
steering_pressed = abs(to_signed((m.dat[1] << 8) | m.dat[2], 16)) > STEER_THRESHOLD
if engaged and torque_cmd is not None and eps_torque is not None and not steering_pressed:
cmds.append(torque_cmd)
eps.append(eps_torque)
else:
if len(cmds) > MIN_SAMPLES:
break
cmds, eps = [], []
if len(cmds) < MIN_SAMPLES:
raise Exception("too few samples found in route")
lm = linear_model.LinearRegression(fit_intercept=False)
lm.fit(np.array(cmds).reshape(-1, 1), eps)
scale_factor = 1. / lm.coef_[0]
if plot:
plt.plot(np.array(eps) * scale_factor)
plt.plot(cmds)
plt.show()
return scale_factor
if __name__ == "__main__":
lr = LogReader(sys.argv[1])
n = get_eps_factor(lr, plot="--plot" in sys.argv)
print("EPS torque factor: ", n)