Reinforcement Learning for Lane-Changing Decision Making in Autonomous Vehicles: A Survey

Autonomous lane-changing is one of the most critical and complex tasks in automated driving. Recent progress in reinforcement learning (RL) has shown strong potential to help autonomous vehicles (AVs) make safe and flexible lane-change decisions in real time under uncertain traffic conditions. In the current studies, there is a lack of a common structure that links RL algorithms, simulation tools, and performance evaluation methods. This paper presents a detailed examination of RL-based lane-changing systems in AVs, tracing their development from early rule-based models to modern learning-based approaches. It introduces a clear classification of lane-changing types—discretionary, mandatory, cooperative, and emergency—and connects each to the most suitable RL methods, including value-based, policy-based, actor–critic, model-based, and hybrid algorithms.

https://www.mdpi.com/2624-6511/9/1/9