With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by experts, and learn to minimize the discrepancy between their actions and expert actions. However, this objective of "only driving like the expert" suffers from limited generalization: when encountering rare or unseen long-tail scenarios outside the distribution of expert demonstrations, models tend to produce unsafe decisions in the absence of prior experience. This raises a fundamental question: Can an E2E-AD system make reliable decisions without any expert action supervision? Motivated by this, we propose a unified framework named Risk-aware World Model Predictive Control (RaWMPC) to address this generalization dilemma through robust control, without reliance on expert demonstrations. Practically, RaWMPC leverages a world model to predict the consequences of multiple candidate actions and selects low-risk actions through explicit risk evaluation. To endow the world model with the ability to predict the outcomes of risky driving behaviors, we design a risk-aware interaction strategy that systematically exposes the world model to hazardous behaviors, making catastrophic outcomes predictable and thus avoidable. Furthermore, to generate low-risk candidate actions at test time, we introduce a self-evaluation distillation method to distill riskavoidance capabilities from the well-trained world model into a generative action proposal network without any expert demonstration. Extensive experiments show that RaWMPC outperforms state-of-the-art methods in both in-distribution and out-of-distribution scenarios, while providing superior decision interpretability.
StereoCarla: A High-Fidelity Driving Dataset for Generalizable Stereo
School of Computer Science, Wuhan University（武汉大学计算机学院）；IAIR, Xi’an Jiaotong University（西安交通大学IAIR）；Waytous；CASIA（中国科学院自动化所）；University of Bologna（博洛尼亚大学）；University of California, Berkeley（加州大学伯克利分校）；AIR, Tsinghua University（清华大学AIR）
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；The University of Hong Kong（香港大学）；Tongji University（同济大学）；Li Auto Inc.（理想汽车）；Huazhong University of Science and Technology（华中科技大学）；Imperial College London（伦敦帝国理工学院）；University of Surrey（萨里大学）
GenAD: Generalized Predictive Model for Autonomous Driving
Hong Kong University of Science and Technology（香港科技大学）；University of Hong Kong（香港大学）；University of Tübingen（蒂宾根大学）；Tübingen AI Center（蒂宾根人工智能中心）；OpenDriveLab（开放驾驶实验室）；Shanghai AI Lab（上海人工智能实验室）
Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications
Mechatronics group at the Department of Electrical Engineering, Chalmers University of Technology（楚克大学电气工程系机电组）；Dept. of Electrical and Information Engineering, Italian National Ph.D. DAUSY, Polytechnic of Bari（意大利国家博士项目DAUSY巴里理工学院电子与信息工程系）
GS-Net: Heterogeneous Vehicle Data Reuse via Generalizable Plug-and-Play 3DGS Module
Tsinghua University（清华大学）；The School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility学院）；The State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）；The College of AI, Tsinghua University（清华大学人工智能学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations
Contextual Robotics Institute, University of California San Diego（加州大学圣地亚哥分校语境机器人研究所）；Faculty of the Department of Computer Science and Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程学院）
DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving
The Hong Kong University of Science & Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science & Technology（香港科技大学）；University of California, Berkeley（加州大学伯克利分校）；PhiGent Robotics（菲境机器人）
Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control
School of Electrical & Computer Engineering, Univ. of Georgia（佐治亚大学电气与计算机工程学院）；Dept. of Electrical & Computer Engineering, Univ. of Central Florida（中佛罗里达大学电气与计算机工程系）
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
Hong Kong University of Science and Technology（香港科技大学）；OpenDriveLab at Shanghai AI Lab（上海人工智能实验室OpenDriveLab）；University of Hong Kong（香港大学）；University of Tübingen（蒂宾根大学）；Tübingen AI Center（蒂宾根人工智能中心）
