Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part II: Assessment Results
University of Michigan Transportation Research Institute（密歇根大学交通研究所）；University of Michigan（密歇根大学）；The University of Hong Kong（香港大学）；Laplace Intelligence（拉普拉斯智能公司）；Tsinghua University（清华大学）
VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving
City University of Hong Kong（香港城市大学）；Soochow University（苏州大学）；McGill University（麦吉尔大学）；Hon Hai Research Institute（鸿海研究院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions over the Internet
Jinan University（暨南大学）；University of Tunis El Manar（突尼斯马纳尔大学）；National Institute of Applied Sciences and Technology, University of Carthage（迦太基大学应用科学与技术国家研究所）；Prince Sultan University（苏丹王子大学）；CISTER/INESC TEC；ISEP-IPP（波尔图理工学院工程学院）；National School of Electronics and Telecommunication（国立电子与电信学院）
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part I: Methodology
University of Michigan Transportation Research Institute（密歇根大学交通研究所）；University of Michigan（密歇根大学）；The University of Hong Kong（香港大学）；Laplace Intelligence（拉普拉斯智能）；Tsinghua University（清华大学）
The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Vehicle Safety Institute, Graz University of Technology（格拉茨技术大学车辆安全研究所）；State Key Laboratory of Automotive Simulation and Control, Jilin University（吉林大学汽车仿真与控制国家重点实验室）
Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment
College of Automotive and Energy Engineering, Tongji University（同济大学汽车与能源工程学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）
Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks
Aschaffenburg University of Applied Sciences（亚斯法恩堡应用科学大学）；HUGIN EXPERT A/S（HUGIN EXPERT公司）；Department of Computer Science, Aalborg University（计算机科学系，奥胡斯大学）
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Zhongguancun Laboratory（中关村实验室）；Beihang University（北京航空航天大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；A*STAR（新加坡科技研究局）
Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps
Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA（加州大学伯克利分校土木与环境工程系）；D3 Analytics Innovation, Allstate Insurance Company, Northbrook, IL, USA（Allstate保险公司D3 Analytics创新部门）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA（加州大学伯克利分校电气工程与计算机科学系）
Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study
Clemson University（克莱姆森大学）；Northern Arizona University（北亚利桑那大学）；The University of Arizona（亚利桑那大学）；Arizona Department of Transportation（亚利桑那州交通局）；Exponent Co（Exponent公司）
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments
Digital Transit Limited, 3M Buckley Innovation Centre, UK, HD1 3BD（数字交通有限公司，3M Buckley创新中心，英国，HD1 3BD）；Department of Computer Science, University of Huddersfield, UK, HD1 3DH（计算机科学系，赫德瑟菲尔德大学，英国，HD1 3DH）
Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making
Machine Intelligence, Interaction, and Imagination (Mi3) Laboratory（机器智能、交互与想象实验室）；Laboratory for Intelligent and Safe Automobiles (LISA)（智能与安全汽车实验室）；Johns Hopkins University（约翰霍普金斯大学）；Center for Medicinal Cannabis Research (CMCR)（医药大麻研究中心）
