自动驾驶

CounterScene: Counterfactual Causal Reasoning in Generative World Models for Safety-Critical Closed-Loop Evaluation

CounterScene: 生成世界模型中的反事实因果推理用于安全关键闭环评估

Bowen Jing, Ruiyang Hao, Weitao Zhou, Haibao Yu

机构 * Tuojing Intelligence（途京智能） ； King's College London（伦敦大学国王学院） ； Tsinghua University（清华大学） ； The University of Hong Kong（香港大学） 纠错

专题命中 端到端驾驶 ：BEV(abstract)；分类 cs.RO、cs.CV

AI总结 CounterScene通过结构化反事实推理生成安全关键驾驶场景，通过因果对抗代理识别和冲突感知交互世界模型，提升长周期碰撞率并保持轨迹真实性。

Comments 28 pages, 7 figures

Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning

自动驾驶场景的自动课程学习：迈向更鲁棒和高效的强化学习

Ahmed Abouelazm, Tim Weinstein, Tim Joseph, Philip Schörner, J. Marius Zöllner

机构 * FZI Research Center for Information Technology（弗赖堡信息科技研究中心） ； Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院） 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

AI总结 本文提出了一种自动课程学习框架，通过动态生成适应性复杂的驾驶场景，提升强化学习在自动驾驶中的鲁棒性和效率。

Comments Accepted in the 36th IEEE Intelligent Vehicles Symposium (IV 2025)

RAP: 3D Rasterization Augmented End-to-End Planning

RAP: 3D 像素化增强端到端规划

Lan Feng, Yang Gao, Eloi Zablocki, Quanyi Li, Wuyang Li, Sichao Liu, Matthieu Cord, Alexandre Alahi

机构 * EPFL（苏黎世联邦理工学院） ； Sorbonne Université（索邦大学） 纠错

专题命中 端到端驾驶 ：end-to-end driving(abstract)；分类 cs.RO、cs.CV

AI总结 RAP通过3D像素化增强端到端规划，利用轻量级像素化和特征对齐实现可扩展的数据增强，提升闭环鲁棒性和长尾泛化能力。

Astra: General Interactive World Model with Autoregressive Denoising

Astra：通用交互世界模型与自回归去噪

Yixuan Zhu, Jiaqi Feng, Wenzhao Zheng, Yuan Gao, Xin Tao, Pengfei Wan, Jie Zhou, Jiwen Lu

机构 * Tsinghua University（清华大学） ； Kuaishou Technology（快手科技） 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

AI总结 Astra提出了一种通用交互世界模型，通过自回归去噪和动作感知适配器实现长周期视频预测与多样化交互。

Comments Accepted in ICLR 2026. Code is available at: https://github.com/EternalEvan/Astra

CrashChat: A Multimodal Large Language Model for Multitask Traffic Crash Video Analysis

CrashChat: 一种多模态大语言模型用于多任务交通事故视频分析

Kaidi Liang, Ke Li, Xianbiao Hu, Ruwen Qin

机构 * Stony Brook University（石溪大学） ； The Pennsylvania State University（宾夕法尼亚州立大学） 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

AI总结 CrashChat是一种多模态大语言模型，用于多任务交通事故视频分析，通过任务解耦和分组策略提升事故识别、定位等任务的性能。

Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models

Orbis：克服驾驶世界模型中长周期预测的挑战

Arian Mousakhan, Sudhanshu Mittal, Silvio Galesso, Karim Farid, Thomas Brox

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

AI总结 Orbis通过简单设计实现自动驾驶世界模型在长周期预测和复杂场景中的高性能，采用连续自回归模型优于离散token模型。

Comments Project page: https://lmb-freiburg.github.io/orbis.github.io/

Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments

Yuhang Peng, Sidong Wang, Jihaoyu Yang, Shilong Li, Han Wang, Jiangtao Gong

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

Comments 7 pages, 9 figures

Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models

Asen Nachkov, Danda Pani Paudel, Jan-Nico Zaech, Davide Scaramuzza, Luc Van Gool

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments Accepted at AAAI 2026

Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs

Dingji Wang, You Lu, Bihuan Chen, Shuo Hao, Haowen Jiang, Yifan Tian, Xin Peng

机构 * College of Computer Science and Artificial Intelligence（计算机科学与人工智能学院） ； Fudan University（复旦大学） 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments The paper has been accepted by the 40th IEEE/ACM International Conference on Automated Software Engineering, ASE 2025

Journal ref Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering.2025

Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training

Zhenxin Li, Shihao Wang, Shiyi Lan, Zhiding Yu, Zuxuan Wu, Jose M. Alvarez

机构 * Institute of Trustworthy Embodied AI, Fudan University（可信具身人工智能研究院，复旦大学） ； Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心） ； The Hong Kong Polytechnic University（香港理工大学） ； NVIDIA 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

MVL-Loc: Leveraging Vision-Language Model for Generalizable Multi-Scene Camera Relocalization

Zhendong Xiao, Wu Wei, Shujie Ji, Shan Yang, Changhao Chen

机构 * School of Automation Science and Engineering, South China University of Technology（自动化科学与工程学院，华南理工大学） ； Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（人工智能方向，香港科学与技术大学（广州）） 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

Comments PRCV

A Survey on World Models Grounded in Acoustic Physical Information

Xiaoliang Chen, Le Chang, Xin Yu, Yunhe Huang, Xianling Tu

机构 * SoundAI Technology Co., Ltd.（声AI技术有限公司） 纠错

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments 28 pages,11 equations

Learnable Burst-Encodable Time-of-Flight Imaging for High-Fidelity Long-Distance Depth Sensing

Manchao Bao, Shengjiang Fang, Tao Yue, Xuemei Hu

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、eess.IV

Learning to Drive from a World Model

Mitchell Goff, Greg Hogan, George Hotz, Armand du Parc Locmaria, Kacper Raczy, Harald Schäfer, Adeeb Shihadeh, Weixing Zhang, Yassine Yousfi

专题命中 端到端驾驶 ：self-driving(abstract)；分类 cs.RO、cs.CV

Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies

Anant Garg, K Madhava Krishna

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments Submitted to IROS 2025

InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model

Feeza Khan Khanzada, Jaerock Kwon

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments This work has been submitted to IROS 2025 and is currently under review

WorldModelBench: Judging Video Generation Models As World Models

Dacheng Li, Yunhao Fang, Yukang Chen, Shuo Yang, Shiyi Cao, Justin Wong, Michael Luo, Xiaolong Wang, Hongxu Yin, Joseph E. Gonzalez, Ion Stoica, Song Han, Yao Lu

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

Dream to Drive with Predictive Individual World Model

Yinfeng Gao, Qichao Zhang, Da-wei Ding, Dongbin Zhao

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

Comments Codes: https://github.com/gaoyinfeng/PIWM

End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning

Mahmoud M. Kishky, Hesham M. Eraqi, Khaled F. Elsayed

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments NCTA 2024 Best Paper Honorable Mention

Journal ref The 16th International Joint Conference on Computational Intelligence (NCTA), Porto, Portugal, November 19-22, 2024

Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, Hongyang Li

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

Comments NeurIPS 2024. Code and model: https://github.com/OpenDriveLab/Vista, demo page: https://vista-demo.github.io

RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation

Manthan Patel, Jonas Frey, Deegan Atha, Patrick Spieler, Marco Hutter, Shehryar Khattak

专题命中 端到端驾驶 ：LiDAR(abstract)；分类 cs.RO、cs.CV

Comments Under review for IEEE RA-L

Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing

Andrew Murdoch, Johannes Cornelius Schoeman, Hendrik Willem Jordaan

专题命中 端到端驾驶 ：end-to-end driving(abstract)；分类 cs.RO、cs.AI

Comments Submitted to IEEE Transactions on Intelligent Transport Systems

Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark

Xin Lin, Jingtong Yue, Sixian Ding, Chao Ren, Lu Qi, Ming-Hsuan Yang

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、eess.IV

ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers

Liangliang Chen, Shiyu Jin, Haoyu Wang, Liangjun Zhang

专题命中 端到端驾驶 ：LiDAR(abstract)；分类 cs.RO、cs.AI

Comments ICRA Workshop 2024: 3rd Workshop on Future of Construction: Lifelong Learning Robots in Changing Construction Sites

End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving

Satya R. Jaladi, Zhimin Chen, Narahari R. Malayanur, Raja M. Macherla, Bing Li

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments Accepted by ITSC

Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging

Bhargav Ghanekar, Salman Siddique Khan, Pranav Sharma, Shreyas Singh, Vivek Boominathan, Kaushik Mitra, Ashok Veeraraghavan

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.CV、eess.IV

Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour

Rhys Howard, Lars Kunze

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Comments 26 Pages = 13 Pages (Main Content) + 5 Pages (References) + 8 Pages (Appendix), 2 Figures, To be published in the Proceedings of the 2nd Conference on Causal Learning and Reasoning as part of the Journal of Machine Learning Research Workshop and Conference Proceedings series, Final submission version; Updated from initial to final submission version

Journal ref Proceedings of the Second Conference on Causal Learning and Reasoning, PMLR 213:473-498, 2023

Autonomous Navigation of Tractor-Trailer Vehicles through Roundabout Intersections

Daniel Attard, Josef Bajada

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

Journal ref TACTFUL 2023

Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning

Jialong Wu, Haoyu Ma, Chaoyi Deng, Mingsheng Long

专题命中 端到端驾驶 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

Comments NeurIPS 2023. Code is available at https://github.com/thuml/ContextWM

CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression

Chen-Hsiu Huang, Ja-Ling Wu

专题命中 端到端驾驶 ：self-driving(abstract)；分类 cs.CV、eess.IV

Comments 7 pages, 5 figures; accepted by APSIPA ASC 2023