We present HetroD, a dataset and benchmark for developing autonomous driving systems in heterogeneous environments. HetroD targets the critical challenge of navi- gating real-world heterogeneous traffic dominated by vulner- able road users (VRUs), including pedestrians, cyclists, and motorcyclists that interact with vehicles. These mixed agent types exhibit complex behaviors such as hook turns, lane splitting, and informal right-of-way negotiation. Such behaviors pose significant challenges for autonomous vehicles but remain underrepresented in existing datasets focused on structured, lane-disciplined traffic. To bridge the gap, we collect a large- scale drone-based dataset to provide a holistic observation of traffic scenes with centimeter-accurate annotations, HD maps, and traffic signal states. We further develop a modular toolkit for extracting per-agent scenarios to support downstream task development. In total, the dataset comprises over 65.4k high- fidelity agent trajectories, 70% of which are from VRUs. HetroD supports modeling of VRU behaviors in dense, het- erogeneous traffic and provides standardized benchmarks for forecasting, planning, and simulation tasks. Evaluation results reveal that state-of-the-art prediction and planning models struggle with the challenges presented by our dataset: they fail to predict lateral VRU movements, cannot handle unstructured maneuvers, and exhibit limited performance in dense and multi-agent scenarios, highlighting the need for more robust approaches to heterogeneous traffic. See our project page for more examples: https://hetroddata.github.io/HetroD/
Decentralized traffic management of autonomous drones
Eötvös University of Budapest（布达佩斯厄特沃什大学）；MTA-ELTE Statistical and Biological Physics Research Group（匈牙利科学院-厄特沃什大学统计与生物物理研究组）；CollMot Robotics LLC（CollMot机器人有限责任公司）
VRUD: A Drone Dataset for Complex Vehicle-VRU Interactions within Mixed Traffic
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University（吉林大学汽车底盘集成与仿生全国重点实验室）；DRIVEResearch；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）
FLUID: A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectories
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Guangdong Provincial Key Laboratory of Intelligent Transportation Systems（广东省智能交通系统重点实验室）；Pengcheng Laboratory（鹏城实验室）
Developing Fundamental Diagrams for Urban Air Mobility Traffic Based on Physical Experiments
Department of Civil and Environmental Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校土木与环境工程系）；Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校土木与环境工程系）；Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校土木与环境工程系）
Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis
Zachry Department of Civil and Environmental Engineering, Texas A&M University（土木与环境工程系，德克萨斯A&M大学）；Department of Landscape Architecture and Urban Planning（景观建筑与城市规划系）
Enhanced Vehicle Speed Detection Considering Lane Recognition Using Drone Videos in California
Department of Computer Science, University of California, Davis（计算机科学系，加州大学戴维斯分校）；Department of Civil and Environmental Engineering, University of California, Davis（土木与环境工程系，加州大学戴维斯分校）
Right in Time: Reactive Reasoning in Regulated Traffic Spaces
Artificial Intelligence and Machine Learning Lab, Department of Computer Science, TU Darmstadt（人工智能与机器学习实验室，计算机科学系，图林根大学）；Konrad Zuse School of Excellence in Learning and Intelligent Systems（Konrad Zuse 学校，卓越学习与智能系统）；Honda Research Institute Europe GmbH（本田欧洲研究院）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；Centre for Cognitive Science（认知科学中心）；German Center for Artificial Intelligence (DFKI)（德国人工智能中心）；Uncertainty in Artificial Intelligence Group, Department of Mathematics and Computer Science, TU Eindhoven（人工智能不确定性小组，数学与计算机科学系，埃因霍温理工大学）
DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery
Department of Electrical Engineering and Information Technology, Intelligent Vehicles Lab (IVL), Munich University of Applied Science（电气工程与信息学院，智能车辆实验室（IVL），慕尼黑应用技术大学）
SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles
State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Civil and Environmental Engineering, University of Macau（澳门大学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）
Improving the Intelligent Driver Model by Incorporating Vehicle Dynamics: Microscopic Calibration and Macroscopic Validation
Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS)（斯图加特汽车工程与车辆发动机研究所（FKFS））；Institute of Automotive Engineering (IFS), University of Stuttgart（斯图加特大学汽车工程研究所（IFS））
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
University of Macau（澳门大学）；University of Electronic Science and Technology of China（电子科技大学）；Peking University（北京大学）；Chongqing University（重庆大学）；Tsinghua University（清华大学）
SinD 2.0: A Multi-City UAV Dataset with Semantic Risk Annotations for SOTIF-Oriented Safety Validation at Signalized Intersections
Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Guangzhou Automobile Group Co., Ltd.（广州汽车集团股份有限公司）；Jilin University（吉林大学）；Chang’an University（长安大学）；Chongqing University（重庆大学）；Southwest University（西南大学）；Wuhan University of Technology（武汉理工大学）；Virginia Tech Transportation Institute（弗吉尼亚理工大学交通研究所）；Virginia Tech（弗吉尼亚理工大学）
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
University of California, Berkeley（加州大学伯克利分校）；Xi’an Jiaotong University（西安交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；MINES ParisTech（巴黎高等矿业学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
