comma.ai实践2

关于longcontrol的说明，acc的原理是根据前车的距离和速度计算本车的加速度，根据本车的速度和加速度通过一个三层深度学习网络算出油门和刹车量，详见如下函数：

def get_compute_gb():   # see debug/dump_accel_from_fiber.py   w0 = np.array([[ 1.22056961, -0.39625418,  0.67952657],                  [ 1.03691769,  0.78210306, -0.41343188]])   b0 = np.array([ 0.01536703, -0.14335321, -0.26932889])   w2 = np.array([[-0.59124422,  0.42899439,  0.38660881],                  [ 0.79973811,  0.13178682,  0.08550351],                  [-0.15651935, -0.44360259,  0.76910877]])   b2 = np.array([ 0.15624429,  0.02294923, -0.0341086 ])   w4 = np.array([[-0.31521443],                  [-0.38626176],                  [ 0.52667892]])   b4 = np.array([-0.02922216])   def compute_output(dat, w0, b0, w2, b2, w4, b4):     m0 = np.dot(dat, w0) + b0     m0 = leakyrelu(m0, 0.1)     m2 = np.dot(m0, w2) + b2     m2 = leakyrelu(m2, 0.1)     m4 = np.dot(m2, w4) + b4     return m4   def leakyrelu(x, alpha):     return np.maximum(x, alpha * x)   def _compute_gb(dat):     #linearly extrap below v1 using v1 and v2 data     v1 = 5.     v2 = 10.     vx = dat[1]     if vx > 5.:       m4 = compute_output(dat, w0, b0, w2, b2, w4, b4)     else:       dat[1] = v1       m4v1 = compute_output(dat, w0, b0, w2, b2, w4, b4)       dat[1] = v2       m4v2 = compute_output(dat, w0, b0, w2, b2, w4, b4)       m4 = (vx - v1) * (m4v2 - m4v1) / (v2 - v1) + m4v1     return m4   return _compute_gb # takes in [desired_accel, current_speed] -> [-1.0, 1.0] where -1.0 is max brake and 1.0 is max gas

compute_gb = get_compute_gb()

而且我遇到了一个坑，刚开始我找了好几天gas命令，监视汽车can的包没有发现gas命令包，不知下一步怎么办。但后来一想可能思域这个车就不需要控制油门，有定速巡航PCM模块自己控制