A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
Technical University of Munich（慕尼黑工业大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Professorship of Autonomous Vehicle Systems（自主车辆系统教授席位）
Rainer Trauth, Alexander Hobmeier, Johannes Betz
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8 Pages. Submitted in Conference IEEE IV 2024 Korea
This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system. The combination directly addresses the challenges of adaptability and safety in autonomous driving. Motion planning algorithms are essential for navigating dynamic and complex scenarios. Traditional methods, however, lack the flexibility required for unpredictable environments, whereas machine learning techniques, particularly reinforcement learning (RL), offer adaptability but suffer from instability and a lack of explainability. Our unique solution synergizes the predictability and stability of traditional motion planning algorithms with the dynamic adaptability of RL, resulting in a system that efficiently manages complex situations and adapts to changing environmental conditions. Evaluation of our integrated approach shows a significant reduction in collisions, improved risk management, and improved goal success rates across multiple scenarios. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-RL.
PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction
The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）；Sun Yat-Sen University（中山大学）
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
The Hong Kong University of Science and Technology（香港科技大学）；Division of Emerging Interdisciplinary Areas（新兴跨学科领域）；Robotics and Autonomous Systems Thrust（机器人与自主系统方向）；Department of Electronic and Computer Engineering（电子与计算机工程系）
Planning by Simulation: Motion Planning with Learning-based Parallel Scenario Prediction for Autonomous Driving
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente（代尔夫特理工大学电子工程、数学与计算机科学学院）；Department of Electrical Engineering, Wright State University（威斯汀大学电气工程系）
An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems
School of Transportation and Logistics, Southwest Jiaotong University（西南交通大学交通运输与物流学院）；University of Central Florida（中佛罗里达大学）；Lyles School of Civil Engineering, Purdue University（普渡大学莱尔土木工程学院）
Residual Chain Prediction for Autonomous Driving Path Planning
Technical University of Munich（慕尼黑工业大学）；School of Computer Science, Nanjing University of Information Science & Technology（南京信息工程大学计算机学院）；School of Computer and Information Engineering, Fuyang Normal University（阜阳师范大学计算机与信息工程学院）
针对端到端自动驾驶路径规划中行为克隆因曼哈顿距离产生协变量偏移的问题，提出 Residual Chain Loss 动态调整损失以增强时序依赖，并在 nuScenes 上验证其性能提升。
NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA（杰米·沃克’66机械工程系，德克萨斯A&M大学，学院站，TX 77843，美国）；The Department of Engineering Technology and Industrial Distribution Texas A&M University, College Station, TX 77843, USA（工程技术与工业分配系，德克萨斯A&M大学，学院站，TX 77843，美国）
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
College of Automotive and Energy Engineering, Tongji University（同济大学汽车与能源工程学院）；Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University（教育部道路与交通工程重点实验室）；Department of Aerospace Engineering, University of Michigan（密歇根大学航空航天工程系）；Hagler Institute for Advanced Study, Texas A&M University（德克萨斯大学A&M分校哈格勒先进研究学院）
LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Horizon Robotics（地平线机器人）
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
FocalAD: Local Motion Planning for End-to-End Autonomous Driving
School of Transportation Science and Engineering（交通科学与工程学院）；State Key Lab of Intelligent Transportation System（智能交通运输系统国家重点实验室）；Hangzhou International Innovation Institute（杭州国际创新研究院）；School of Computer Science and Technolog（计算机科学与技术学院）；Beijing Jiaotong University（北京交通大学）
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；Hong Kong Baptist University（香港浸会大学）；Hubei University（湖北大学）；Hunan University（湖南大学）；University of Chinese Academy of Sciences（中国科学院大学）；Jinan University（暨南大学）
