Exploring applications of deep reinforcement learning for real-world autonomous driving systems
This is an incremental review paper for researchers and practitioners in autonomous driving, highlighting existing limitations without presenting new methods or results.
The paper tackles the gap between deep reinforcement learning (DRL) research in synthetic environments and its real-world deployment in autonomous driving systems, aiming to encourage practical applications by providing an overview and discussing challenges.
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.