Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs
This work addresses the planning problem for autonomous vehicles, but it is incremental as it provides a baseline for future research rather than a major breakthrough.
The paper tackled the lack of machine learning in autonomous vehicle planning by designing a recurrent neural network for motion planning with high-dimensional inputs, aiming to enable more human-like driving behaviors.
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.