A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
It provides a systematic overview for researchers and practitioners in autonomous driving, but is incremental as it surveys existing work without new results.
This survey summarizes reinforcement learning applications for autonomous vehicle motion planning and control, highlighting a shift from pipeline approaches to end-to-end methods that offer better performance and smaller scales, though challenges like data scarcity and generalization persist.
In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. Many existing contributions can be attributed to the pipeline approach, which consists of many hand-crafted modules, each with a functionality selected for the ease of human interpretation. However, this approach does not automatically guarantee maximal performance due to the lack of a system-level optimization. Therefore, this paper also presents a growing trend of work that falls into the end-to-end approach, which typically offers better performance and smaller system scales. However, their performance also suffers from the lack of expert data and generalization issues. Finally, the remaining challenges applying deep RL algorithms on autonomous driving are summarized, and future research directions are also presented to tackle these challenges.