  The use of machine learning in the self-driving industry has boosted a number
of recent advancements. In particular, the usage of large deep learning models
in the perception and prediction stack have proved quite successful, but there
still lacks significant literature on the use of machine learning in the
planning stack. The current state of the art in the planning stack often relies
on fast constrained optimization or rule-based approaches. Both of these
techniques fail to address a significant number of fundamental problems that
would allow the vehicle to operate more similarly to that of human drivers. In
this paper, we attempt to design a basic deep learning system to approach this
problem. Furthermore, the main underlying goal of this paper is to demonstrate
the potential uses of machine learning in the planning stack for autonomous
vehicles (AV) and provide a baseline work for ongoing and future research.
