Highway platoon merging control using RL: a review

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lane change, reinforcement learning, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The field of autonomous driving continues to be the center of attention in artificial intelligence and robotics research. Deep Reinforcement Learning (DRL) has emerged as a promising approach for training Autonomous Vehicles (AVs) to navigate complex real-world scenarios. This chapter examines recent progress in applying DRL to highway lane changes, ramp merges, and platoon coordination. It analyzes similarities, differences, limitations, and best practices in DRL formulations, training algorithms, simulations, and performance metrics across these applications. The chapter begins with an overview of various traffic scenarios discussed in the literature. It then delves into DRL technology, focusing on two key aspects: state representation methods that capture critical interactive dynamics for safe and efficient merging, and reward formulations that balance key metrics such as safety, efficiency, comfort, and adaptability. The insights from this comprehensive review aim to inform future research directions, ultimately advancing the potential of DRL in automated driving within complex and uncertain traffic environments.

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