Physics-informed Imitative Reinforcement Learning for Real-world Driving
Robotics and Autonomous Systems Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统 thrust、系统枢纽）；Department of Computer Science and Engineering, School of Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系、工程学院）
Recent advances in imitative reinforcement learning (IRL) have considerably enhanced the ability of autonomous agents to assimilate expert demonstrations, leading to rapid skill acquisition in a range of demanding tasks. However, such learning-based agents face significant challenges when transferring knowledge to highly dynamic closed-loop environments. Their performance is significantly impacted by the conflicting optimization objectives of imitation learning (IL) and reinforcement learning (RL), sample inefficiency, and the complexity of uncovering the hidden world model and physics. To address this challenge, we propose a physics-informed IRL that is entirely data-driven. It leverages both expert demonstration data and exploratory data with a joint optimization objective, allowing the underlying physical principles of vehicle dynamics to emerge naturally from the training process. The performance is evaluated through empirical experiments and results exceed popular IL, RL and IRL algorithms in closed-loop settings on Waymax benchmark. Our approach exhibits 37.8% reduction in collision rate and 22.2% reduction in off-road rate compared to the baseline method.
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（印度科学学院罗伯特·博世信息物理系统中心）；Indian Institute of Science（印度科学学院）；PES university（PES大学）
VEGA: Electric Vehicle Navigation Agent via Physics-Informed Neural Operator and Proximal Policy Optimization
Department of Mechanical Engineering, State University of New York（机械工程系，纽约州立大学）；Department of Computer Science, State University of New York（计算机科学系，纽约州立大学）
Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China（北京理工大学机械工程学院）；School of Mechatronics Engineering, Changwon National University（昌原国立大学机电工程学院）
Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；Li Auto Inc.（利亚德公司）；UC Berkeley（加州大学伯克利分校）
A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models
School of Advanced Technology（先进技术学院）；Institute of Transport Studies（交通研究所）；Department of Computer Science（计算机科学系）；City University of Hong Kong（香港城市大学）
Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；The University of Hong Kong（香港大学）；Shenzhen University（深圳大学）
Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks
School of Vehicle and Mobility, and State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China（车辆与移动系统学院和智能绿色车辆与移动系统国家重点实验室，清华大学，北京，中国）；KargoBot Inc., Beijing, China（KargoBot公司，北京，中国）
A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets
Institute for Data, Systems, and Society, Massachusetts Institute of Technology（麻省理工学院数据、系统与社会研究所）；Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电气工程与计算机科学系）；Department of Civil and Environmental Engineering, Massachusetts Institute of Technology（麻省理工学院土木工程与环境工程系）
Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学上海智能科学与技术研究院）；Department of Electrical and Computer Engineering, Technical University of Munich（慕尼黑技术大学电子与计算机工程系）
Data-driven Camera and Lidar Simulation Models for Autonomous Driving: A Review from Generative Models to Volume Renderers
WMG, University of Warwick（华威大学WMG学院）；School of Electronics, Electrical Engineering and Computer Science at Queen’s University of Belfast（贝尔法斯特女王大学电子、电气工程与计算机科学学院）
Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；NVIDIA Research, NVIDIA Corporation（英伟达研究院，英伟达公司）；Department of Aeronautics and Astronautics, Stanford University（斯坦福大学航空航天系）
