Accepted and published in IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 6, pp. 7807-7818, June 2025. DOI: 10.1109/TITS.2025.3556543
发表信息：
IEEE Trans. Intell. Transp. Syst., vol. 26, no. 6, pp. 7807-7818, June 2025
Previous studies in predicting crash risks primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics, usually neglecting the impact of vehicles' continuous movement and interactions with nearby vehicles. Recent technology advances, such as Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs) are able to collect high-resolution trajectory data, which enables trajectory-based risk analysis. This study investigates a new vehicle group (VG) based risk analysis method and explores risk evolution mechanisms considering VG features. An impact-based vehicle grouping method is proposed to cluster vehicles into VGs by evaluating their responses to the erratic behaviors of nearby vehicles. The risk of a VG is aggregated based on the risk between each vehicle pair in the VG, measured by inverse Time-to-Collision (iTTC). A Logistic Regression and a Graph Neural Network (GNN) are then employed to predict VG risks using aggregated and disaggregated VG information. Both methods achieve excellent performance with AUC values exceeding 0.93. For the GNN model, GNNExplainer with feature perturbation is applied to identify critical individual vehicle features and their directional impact on VG risks. Overall, this research contributes a new perspective for identifying, predicting, and interpreting traffic risks.
Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: association knowledge using data mining approach
University of Louisiana at Lafayette（路易斯安那大学拉法叶分校）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Rajshahi Science & Technology University（拉杰沙希科技大学）；Auburn University（奥本大学）
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
College of Automotive Engineering, Chongqing University（重庆大学汽车工程学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 mobility学院）；University of Waterloo（滑铁卢大学）
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
School of Mathematics and Statistics, Yangtze Normal University（长江师范学院数学与统计学院）；College of Automotive Engineering, Chongqing University（重庆大学汽车工程学院）；University of Waterloo（滑铁卢大学）
Data-Driven Analysis of Crash Patterns in SAE Level 2 and Level 4 Automated Vehicles Using K-means Clustering and Association Rule Mining
Collier County Government Traffic Management Center（科利尔县政府交通管理中心）；Department of Computer Science and Engineering University of Tennessee at Chattanooga（田纳西大学查塔努加分校计算机科学与工程系）；Center of Urban Informatics and Progress（城市信息化与进步中心）
From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps
C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems（C-DRiVeS实验室：车载系统认知驾驶研究）；Computer Science and Engineering Department - Faculty of Media Engineering and Technology - German University in Cairo（计算机科学与工程系 - 媒体工程与技术学院 - 埃及德国大学）
BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China
VanJee Technology（万集科技）；BeiJing Academy of Artificial Intelligence（北京人工智能研究院）
随着路边感知在互联自动车辆高速公路（CAVH）技术中发挥越来越重要的作用，迫切需要挑战真实世界的路边数据集，用于基准标记和训练各种计算机视觉任务，如2D/3D目标检测和多传感器融合。本文首先介绍了一个具有挑战性的BAAI-VANJEE路边数据集，该数据集由放置在路边约4.5米高的VANJEE智能基站收集的LiDAR数据和RGB图像组成。该数据集包含2500帧LiDAR数据，5000帧RGB图像，其中20%同时采集。它还包含12类对象，74 K 3D对象注释和105 K 2D对象注释。通过提供真实复杂的城市交叉口和高速公路场景，我们期待BAAI-VANJEE路边数据集将积极协助学术界和产业界加速大数据时代智能交通领域的创新研究和成果转化。
An Intelligent Monitoring System of Vehicles on Highway Traffic
University of Engineering and Technology Peshawar（白沙瓦工程技术大学）；University of Swabi（斯瓦比大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）；Jessore University of Science and Technology（杰索尔科技大学）
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与运载工程学院）；Department of Industrial Design, Chongqing Normal University（重庆师范大学工业设计系）
Controllable risk scenario generation from human crash data for autonomous vehicle testing
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing DiDi Infinity Technology and Development Co., Ltd.（北京滴滴无限科技发展有限公司）；Space Information Research Institute and Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University（空间信息研究所和浙江空间信息感知与传输重点实验室，杭州电子科技大学）
From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
Department of Civil Engineering（土木工程系）；Rajshahi University of Engineering & Technology（拉贾沙希工程与技术大学）；Traffic Safety Group（交通安全组）；Multimodal Planning Division (MPD)（多模式规划部门）；Arizona Department of Transportation (ADOT)（亚利桑那州交通部门）；Texas Southern University（德克萨斯南方大学）
Fault-Tolerant MARL for CAVs under Observation Perturbations for Highway On-Ramp Merging
Department of Automation, Tsinghua University（自动化系，清华大学）；Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（北京信息科学与技术国家研究中心（BNRist），清华大学）
Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion
Purdue University（普渡大学）；Lyles School of Civil Engineering（莱尔土木工程学院）；Center for Connected and Automated Transportation (CCAT)（连通与自动化交通中心（CCAT））；Carnegie Mellon University（卡内基梅隆大学）；Robotics Institute（机器人研究所）
Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology
Rhodes University（罗德斯大学）；University of Ngaoundéré（恩冈代雷大学）
