Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion
Institute for Physical Artificial Intelligence (IPAI), Purdue University（物理人工智能研究所（IPAI）、普渡大学）；College of Engineering, Purdue University（工程学院、普渡大学）；Department of Computer Science, Purdue University（计算机科学系、普渡大学）
Jiaru Zhang, Manav Gagvani, Can Cui, Juntong Peng, Ruqi Zhang, Ziran Wang
Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged as promising candidates for end-to-end autonomous driving. However, these models typically face challenges in inference latency, action precision, and explainability. Existing autoregressive approaches struggle with slow token-by-token generation, while prior diffusion-based planners often rely on verbose, general-purpose language tokens that lack explicit geometric structure. In this work, we propose Masked Vision-Language-Action Diffusion for Autonomous Driving (MVLAD-AD), a novel framework designed to bridge the gap between efficient planning and semantic explainability via a masked vision-language-action diffusion model. Unlike methods that force actions into the language space, we introduce a discrete action tokenization strategy that constructs a compact codebook of kinematically feasible waypoints from real-world driving distributions. Moreover, we propose geometry-aware embedding learning to ensure that embeddings in the latent space approximate physical geometric metrics. Finally, an action-priority decoding strategy is introduced to prioritize trajectory generation. Extensive experiments on nuScenes and derived benchmarks demonstrate that MVLAD-AD achieves superior efficiency and outperforms state-of-the-art autoregressive and diffusion baselines in planning precision, while providing high-fidelity and explainable reasoning.
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning
School of EIC, Huazhong University of Science & Technology（华中科技大学电子信息与通信学院）；Institute of Artificial Intelligence, Huazhong University of Science & Technology（华中科技大学人工智能研究院）；Horizon Robotics
M-BEV: Masked BEV Perception for Robust Autonomous Driving
Shenzhen Institute of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）；University of Chinese Academy of Science（中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）
DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving
Huazhong University of Science and Technology（华中科技大学）；Xiaomi EV（小米电动车）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）
UniM$^2$AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving
Harbin Institute of Technology（哈尔滨工业大学）；Tianyijiaotong Technology Ltd.（天宜交通科技有限公司）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Institute of Automotive Engineering, Technical University (TU) of Darmstadt（达姆施塔特工业大学汽车工程研究所）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets
University of California San Diego（加利福尼亚大学圣迭戈分校）；Laboratory for Intelligent and Safe Automobiles（智能与安全汽车实验室）
Semantics-Guided Multimodal Masked Autoencoder Pretraining for 3D BEV Object Detection
University of Lincoln, Lincoln Centre for Autonomous Systems（林肯大学，林肯自主系统中心）；University of Cambridge, Institute for Manufacturing, Department of Engineering（剑桥大学，制造研究所，工程系）
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与出行学院）；DiDi Global（滴滴全球公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning
Faculty of Automatic Control and Computer Science, Politehnica University of Bucharest（自动化控制与计算机科学系，布加勒斯特理工大学）；Institute of Mathematics of the Romanian Academy（罗马尼亚科学院数学研究所）
Sparse Prototype Network for Explainable Pedestrian Behavior Prediction
Institutes of Innovation for Future Society, Nagoya University（名古屋大学未来社会创新研究院）；Graduate School of Engineering, Gifu University（岐阜大学工学研究科）；Tier IV Inc.（Tier IV公司）；Nagoya University Open Innovation Center（名古屋大学开放创新中心）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）
