Rethinking Data Augmentation in End-to-End Learning
Suppose the ego position relative to the center lane is denoted as $X$. We do random position augmentation along the lateral direction. Let $Y$ denote the position after augmentation as $Y = X+ Z$, where $Z$ is the random position augmentation. Assume $X\sim p_x$, $Z\sim p_z$ with its probability density function $f_X(x)$ and $f_Z(z)$, respectively. So, the distribution of $Y$ can be computed as below. First, we will compute the accumulated distribution of $Y$, then can compute the density distribution. Let $F_Y(y)$ denote the accumulated distribution of $Y$, then ...