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This survey paper reviews recent advancements in applying Reinforcement Learning (RL) to Autonomous Driving (AD) across high-level decision-making, motion control, and end-to-end systems. It highlights how RL, when combined with techniques like visual-language models, imitation learning, and world models, improves planning accuracy, task success rates, and sim-to-real transfer. The paper also discusses limitations of current RL applications in AD and future research directions like meta-learning and human-machine collaboration.
RL's increasing sophistication is closing the gap between simulated and real-world autonomous driving, but challenges in data dependency, safety, and interpretability remain.
In recent years, with the rapid development of intelligent transportation, Reinforcement Learning (RL), as an adaptive decision-making method, has gradually permeated into various levels of Autonomous Driving (AD). Therefore, this paper reviews the latest advances in the application of RL in AD. In terms of high-level decision-making and behavioral planning, RL, combined with visual-language models, imitation learning, multi-stage training, and autoregressive trajectory planning, systematically improves planning accuracy and task success rates. At the motion control level, the synergistic optimization of deep reinforcement learning (DRL) based continuous control strategies and robust control methods enhances performance in path tracking, dynamic obstacle avoidance, and multi-sensor information fusion. Meanwhile, end-to-end autonomous driving leverages novel frameworks such as closed-loop RL, World Model (WM), and multimodal decision fusion, effectively narrowing the gap between simulation and real-world environments while achieving significant improvements in safety and smoothness. Additionally, the paper discusses the limitations of RL applications, including data dependency, training efficiency, safety, and interpretability. Furthermore, it explores the future prospects for achieving more intelligent autonomous driving systems through strategies such as meta-learning, transfer learning, adversarial training, and human-machine collaboration.
