Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based model-free ADSs have shown promising results. However, pure learning-based approaches lack the hard-coded safety measures of model-based controllers. Here we propose a hybrid approach for integrating a path planning pipe into a vision based DRL framework to alleviate the shortcomings of both worlds. In summary, the DRL agent is trained to follow the path planner's waypoints as close as possible. The agent learns this policy by interacting with the environment. The reward function contains two major terms: the penalty of straying away from the path planner and the penalty of having a collision. The latter has precedence in the form of having a significantly greater numerical value. Experimental results show that the proposed method can plan its path and navigate between randomly chosen origin-destination points in CARLA, a dynamic urban simulation environment. Our code is open-source and available online.
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）
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving
Center for Applied Autonomous Sensor Systems (AASS)（应用自主传感器系统中心）；Örebro University（奥雷布罗大学）；Computer Vision Laboratory（计算机视觉实验室）；Linköping University（利德斯霍尔大学）
Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
University of Science and Technology of China（中国科学技术大学）；Yinwang Intelligent Technology Co., Ltd（银网智能科技有限公司）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）
Rethinking Imitation-based Planner for Autonomous Driving
Hong Kong University of Science and Technology（香港科技大学）；Lotus Technology Ltd.（路特斯科技有限公司）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
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）
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
Technical University of Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；University College London（伦敦大学学院）
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
School of Information Technology, Monash University, Malaysia（墨尔本大学信息科技学院）；Faculty of Information Technology, Monash University, Australia（墨尔本大学信息技术学院）；TCS Research, India（印度TCS研究）
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Intelligent Transportation Thrust, Systems Hub, and Center of Seamless Connectivity & Connected Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（智能交通 thrust、系统中心及无缝连接与智能连接研究院，香港科学与技术大学（广州））
What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners
Netherlands Organisation for Applied Scientific Research（荷兰应用科学研究院）；Integrated Vehicle Safety Group（集成车辆安全组）；Radboud University（拉德堡德大学）；Donders Institute for Brain, Cognition and Behaviour（多纳尔斯脑、认知与行为研究所）
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
Texas A&M University（德克萨斯农工大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Maryland（马里兰大学）；University of California, Riverside（加利福尼亚大学河滨分校）；University of Pittsburgh（匹兹堡大学）
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）
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University（J. Mike Walker ’66 机械工程系，德克萨斯A&M大学）；Department of Engineering technology and Industrial Distribution, Texas A&M University（工程技术与工业分销系，德克萨斯A&M大学）
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Tsinghua University（清华大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
