Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study, we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: https://boyiliee.github.io/llada.
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（自主车辆系统教授职位，TUM工程与设计学院，慕尼黑技术大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））
H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Artificial Intelligence, University of Chinese Academy of Science（中国科学院大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）
LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving
Department of Civil and Environmental Engineering, University of Michigan（土木与环境工程系，密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
Toyota Research Institute（丰田研究院）；Department of Mechanical Engineering, Rice University（机械工程系， Rice大学）；MANE, Rensselaer Polytechnic Institute（MANE，Rensselaer理工学院）
针对极限动态驾驶中在线学习滞后易致失控的问题，提出结合贝叶斯最后一层元学习与不确定性引导主动数据收集的车辆动力学辨识框架，并在 Toyota Supra 漂移实验中验证其可靠控制效果。
Saliency-Guided Domain Adaptation for Left-Hand Driving in Autonomous Steering
Centre for Accident Research and Road Safety-Queensland, Queensland University of Technology（昆士兰事故研究与道路安全中心、昆士兰理工大学）；QUT Centre for Robotics and School of Electrical Engineering and Computer Science, Queensland University of Technology（昆士兰大学机器人中心及电气工程与计算机科学学院、昆士兰理工大学）
Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network
School of Automotive Studies, Tongji University（同济大学汽车学院）；College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）；Clean Energy Automotive Engineering Center, Tongji University（同济大学清洁能源汽车工程中心）
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室、加州理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Georgia Institute of Technology（佐治亚理工学院）
Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）；Collaborative Initiative, Interdisciplinary Graduate Programme, Nanyang Technological University（南洋理工大学跨学科研究生项目合作计划）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models
Bosch Corporate Research, Bosch (China) Investment Ltd.（博世企业研究，博世（中国）投资有限公司）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）
VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving
Department of Civil and Environmental Engineering, The Pennsylvania State University（宾夕法尼亚州立大学土木与环境工程系）；Department of Civil Engineering, Stony Brook University（石溪大学土木工程系）
