近半年「自动驾驶」篇强化学习论文推荐~

Core Viewpoint - The article emphasizes the significant potential of reinforcement learning (RL) in the field of autonomous driving, highlighting its ability to enhance safety, reliability, and intelligence in autonomous vehicles [3][4]. Group 1: Recommended Papers on RL Applications in Autonomous Driving - The article presents a list of the top 10 recommended papers on RL applications in autonomous driving, focusing on practical challenges and innovative solutions [4][7]. - "CarPlanner" is highlighted as a promising solution for trajectory planning in autonomous driving, demonstrating superior performance over state-of-the-art methods in a challenging dataset [9]. - "RAD" introduces a closed-loop RL training paradigm using 3DGS technology, achieving a threefold reduction in collision rates compared to imitation learning methods [10]. - "Toward Trustworthy Decision-Making for Autonomous Vehicles" discusses a robust RL approach with safety guarantees, focusing on collision safety and policy robustness [13]. - "ReCogDrive" combines visual language models with diffusion planners to enhance autonomous driving safety and performance, achieving a new benchmark in trajectory prediction [17]. - "LGDRL" proposes a large language model-guided deep RL framework for decision-making in autonomous driving, achieving a 90% task success rate [23]. - "AlphaDrive" is noted for its innovative use of GRPO-based RL in high-level planning, outperforming traditional methods with only 20% of the data [26]. Group 2: Classic Works in RL for Autonomous Driving - The article references several classic papers that have established the core position of RL in autonomous driving, including a survey on deep RL applications [42]. - "Dense Reinforcement Learning for Safety Validation" addresses challenges in high-dimensional spaces and proposes solutions to enhance safety in autonomous vehicles [42]. - A paper on decision-making strategies for autonomous vehicles in uncertain highway environments demonstrates the effectiveness of deep RL in improving safety and efficiency [44].