Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new "RoadText-1K" dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtext-1k
TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving
Faculty of Marine Science and Technology, Beijing Institute of Technology（海洋科学与技术学院，北京理工大学）；State Key Laboratory of Intelligent Technology and Systems and Department of Computer Science and Technology, Tsinghua University（智能技术与系统国家重点实验室和计算机科学与技术系，清华大学）；School of Engineering and Applied Science, Yale University（工程与应用科学学院，耶鲁大学）；University of Toronto（多伦多大学）；Beijing University of Chemical Technology（北京化工大学）
ResLPR: A LiDAR Data Restoration Network and Benchmark for Robust Place Recognition Against Weather Corruptions
College of Intelligence Science and Technology（智能科学与技术学院）；National University of Defense Technology（国防科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；University of Science and Technology of China（中国科学技术大学）
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving
University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；University of Washington（华盛顿大学）；Can Tho University（芹苴大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
TraRA: Trajectory-level Recognition Aggregation for Video Text Spotting in Urban Surveillance
RIKEN（日本理化学研究所）；Hanoi University of Science and Technology（河内科学技术大学）；Nagoya University（名古屋大学）；Lawrence Technological University（劳伦斯技术大学）；Ritsumeikan University（立命馆大学）
ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB
Department of ICE, Hanbat National University, Daejeon, Republic of Korea（信息电子工程系，翰 bam国立大学）；Department of ECE and INMC, Seoul National University, Seoul, Republic of Korea（电子工程系和智能纳米材料中心，首尔国立大学）；Department of ECE and INMC, Seoul National University（电子工程系和智能纳米材料中心，首尔国立大学）
NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving
ARC Training Centre for Automated Vehicles in Rural and Remote Regions (AVR3)（澳大利亚农村及偏远地区自动驾驶车辆ARC培训中心）；Université Gustave Eiffel（Gustave Eiffel大学）；Queensland University of Technology (QUT)（昆士兰科技大学）
Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition
Institute for Intelligent Systems（智能系统研究所）；Esslingen University of Applied Sciences（应用科学大学埃斯林根）；Department of Computer Science（计算机科学系）；University of Freiburg（弗赖堡大学）
Traffic Sign Recognition in Autonomous Driving: Dataset, Benchmark, and Field Experiment
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Lingnan University（岭大）；Shenzhen Unity Drive Innovation Technology Co., Ltd.（深圳Unity Drive创新技术有限公司）；The Hong Kong University of Science and Technology（香港科技大学）
Spatial-Temporal Perception with Causal Inference for Naturalistic Driving Action Recognition
School of Mechanical Engineering, Nanjing University of Science and Technology（南京理工大学机械工程学院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition
School of Automotive Studies, Tongji University（同济大学汽车学院）；Department of Automation and Perception, Shanghai Jiao Tong University（上海交通大学自动化与感知系）；Zenseact
Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
School of Automotive and Traffic Engineering, Liaoning University of Technology（辽宁科技学院汽车与交通工程学院）；State Key Laboratory of Intelligent Green Vehicles and Mobility, School of Vehicle and Mobility, Tsinghua University（智能绿色车辆与移动State Key Laboratory，清华大学车辆与移动学院）
A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines
Beijing Institute of Technology（北京理工大学）；UC Berkeley（加州大学伯克利分校）
提出了一种快速模式识别方法来表征驾驶员曲线通过行为。为了缩短识别时间，提高驾驶风格的识别率，提出了一种基于k均值聚类的支持向量机（kMC-SVM）方法，并将其用于驾驶员的驾驶风格分类。首先将车速和节气门开度作为反映驾驶风格的特征参数。其次，为了区分驾驶员曲线通过行为，减少支持向量的数量，采用k-均值聚类方法对两类驾驶数据进行提取和聚类，缩短识别时间。然后，基于聚类结果，利用支持向量机方法生成超平面，用于判断和预测人类驾驶员属于哪种类型。最后，为了验证kMC-SVM方法的有效性，设计并进行了交叉验证实验。研究结果表明，与SVM方法相比，$ k $ MC-SVM方法是一种有效的驾驶风格分类方法，分类时间短。
IFTD: Image Feature Triangle Descriptor for Loop Detection in Driving Scenes
Huazhong University of Science and Technology（华中科技大学）；Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）
