A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

Abstract

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.

arXiv comment8 pages

References in corpus (16)

• Continuous control with deep reinforcement learning
• End to End Learning for Self-Driving Cars
• A Survey of Autonomous Driving: Common Practices and Emerging Technologies
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
• Virtual to Real Reinforcement Learning for Autonomous Driving
• Query-Efficient Imitation Learning for End-to-End Autonomous Driving
• Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
• Deep Imitative Models for Flexible Inference, Planning, and Control
• Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
• Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
• Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
• Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
• Deep Reinforcement Learning for Dynamic Urban Transportation Problems
• Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
• Meta learning Framework for Automated Driving
• Exploring applications of deep reinforcement learning for real-world autonomous driving systems