World models have demonstrated significant promise for data synthesis in autonomous driving. However, existing methods predominantly concentrate on single-modality generation, typically focusing on either multi-camera video or LiDAR sequence synthesis. In this paper, we propose UniDriveDreamer, a single-stage unified multimodal world model for autonomous driving, which directly generates multimodal future observations without relying on intermediate representations or cascaded modules. Our framework introduces a LiDAR-specific variational autoencoder (VAE) designed to encode input LiDAR sequences, alongside a video VAE for multi-camera images. To ensure cross-modal compatibility and training stability, we propose Unified Latent Anchoring (ULA), which explicitly aligns the latent distributions of the two modalities. The aligned features are fused and processed by a diffusion transformer that jointly models their geometric correspondence and temporal evolution. Additionally, structured scene layout information is projected per modality as a conditioning signal to guide the synthesis. Extensive experiments demonstrate that UniDriveDreamer outperforms previous state-of-the-art methods in both video and LiDAR generation, while also yielding measurable improvements in downstream
OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Westlake University（西湖大学）；Li Auto Inc.（Li汽车公司）；The University of Toronto（多伦多大学）；University of Macau（澳门大学）
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Horizon Robotics；Nanjing University（南京大学）；Huazhong University of Science & Technology（华中科技大学）；Shanghai AI Laboratory（上海人工智能实验室）
Generating Multimodal Driving Scenes via Next-Scene Prediction
School of Software Engineering, XJTU（西安交通大学软件学院）；Horizon Robotics（地平线机器人）；School of Computer and Communication Sciences, EPFL（洛桑联邦理工学院计算机与通信科学学院）；University of Chinese Academy of Sciences（中国科学院大学）
MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving
Amap, Alibaba Group（阿里集团蚂巴公司）；The Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong（香港中文大学）；Xi’an Jiaotong University（西安交通大学）
OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving
School of Automotive Studies, Tongji University（同济大学汽车学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；AI Foundation（2077AI基金会）；Momoni AI；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市学院信息与电气工程学院）
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Forschungszentrum Informatik（弗劳恩霍夫信息技术研究中心）；Hochschule Esslingen（埃斯林根应用技术大学）；University of Michigan（密歇根大学）；Voxel51；Karlsruher Institut für Technologie（卡尔斯鲁厄理工学院）
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）；Department of Software and Microelectronics, Peking University（软件与微电子系，北京大学）；School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University（虚拟现实技术与系统国家重点实验室，北航软件学院）；Peng Cheng Laboratory（鹏城实验室）；Baidu Inc（百度公司）
Toward Unified Multimodal Representation Learning for Autonomous Driving
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA（德克萨斯大学机械工程系，德克萨斯农工大学，学院站，德克萨斯，77843，美国）；The Department of Engineering Technology and Industrial Distribution Texas A&M University, College Station, TX 77843, USA（工程技术与工业分布系，德克萨斯农工大学，学院站，德克萨斯，77843，美国）
Post-interactive Multimodal Trajectory Prediction for Autonomous Driving
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；Department of Civil Engineering, Monash University（莫纳什大学土木工程系）
MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；HKUST(GZ)（香港科技大学（广州））；Beijing University of Chemical Technology（北京化工大学）；Beihang University（北京航空航天大学）
Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving
Dept. of Information Science University of North Texas Denton, Texas, USA（信息科学系 诺克斯维尔大学 德顿, 德克萨斯州, 美国）；Dept. of Data Science University of North Texas Denton, Texas, USA（数据科学系 诺克斯维尔大学 德顿, 德克萨斯州, 美国）
ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving
Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong（香港中文大学）；Dalian University of Technology（大连理工大学）；TU Delft（代尔夫特理工大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Huawei Zurich Research Center（华为苏黎世研究中心）；Huawei IAS BU（华为IAS业务单元）
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems
Zhejiang University,\ University - Ant \ Joint Laboratory of Knowledge Graph Hangzhou China；Zhejiang University,\ University - Ant \ Joint Laboratory of Knowledge Graph
UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception
Energy and Transportation Domain, Beijing Institute of Technology（能源与交通领域，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清华大学计算机科学与技术系）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（机械与航空航天工程学院，南洋理工大学）；School of Transportation Science and Engineering and the State Key Lab of Intelligent Transportation System, Beihang University（交通运输科学与工程学院和智能交通系统国家重点实验室，北京航空航天大学）；Beijing University of Chemical Technology（北京化工大学）
VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Li Auto Inc.（Li汽车公司）；the School of Aeronautics and Astronautics, Xiamen University（厦门大学航空航天学院）；The Hong Kong University of Science and Technology（香港科技大学）
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
FZI Research Center for Information Technology（弗劳恩霍夫信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Charles III of Madrid（马德里第三大学）；Delft University of Technology（代尔夫特理工大学）
Tracking Meets Large Multimodal Models for Driving Scenario Understanding
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学（MBZUAI））；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
