自动驾驶

Digital Twins for Autonomous Driving: A Comprehensive Implementation and Demonstration

Kui Wang, Tao Yu, Zongdian Li, Kei Sakaguchi, Omar Hashash, Walid Saad

专题命中 端到端驾驶 ：autonomous driving(title,abstract)

Comments 7 pages, 8 figures

CAT: Closed-loop Adversarial Training for Safe End-to-End Driving

Linrui Zhang, Zhenghao Peng, Quanyi Li, Bolei Zhou

专题命中 端到端驾驶 ：end-to-end driving(title,abstract)

Comments 7th Conference on Robot Learning (CoRL 2023)

Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data

Suphanut Jamonnak, Ye Zhao, Xinyi Huang, Md Amiruzzaman

专题命中 端到端驾驶 ：autonomous driving(title,abstract)

Comments 11 pages, 8 figures, and 1 table. This paper is accepted and to be published in IEEE Transactions on Visualization and Computer Graphics

Journal ref IEEE Transactions on Visualization and Computer Graphics, 2021

An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models

Yao Deng, Xi Zheng, Tianyi Zhang, Chen Chen, Guannan Lou, Miryung Kim

专题命中 端到端驾驶 ：autonomous driving(title,abstract)

Self Training Autonomous Driving Agent

Shashank Kotyan, Danilo Vasconcellos Vargas, Venkanna U

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

Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

Ashish Mehta, Adithya Subramanian, Anbumani Subramanian

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

Comments 12 pages, 5 figures, 1 table

CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners

CADET: 基于物理的因果审计与无训练去混杂的端到端驾驶规划器

Zikun Guo, Yuanyuan Li, Rongjin Zou

机构 * School of Electronics Engineering, Kyungpook National University（庆北国立大学电子工程学院） 纠错

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

AI总结 提出CADET框架，无需重新训练即可审计和修复预训练端到端驾驶规划器中的虚假关联，通过物理因果图识别混杂因素并干预测试时输入。

Comments 8pages 4figures

From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving

从表征互补性到双系统：协同VLM和纯视觉骨干网络用于端到端驾驶

Sining Ang, Yuguang Yang, Chenxu Dang, Canyu Chen, Cheng Chi, Haiyan Liu, Xuanyao Mao, Jason Bao, Xuliang, Bingchuan Sun, Yan Wang

机构 * Department of Automation, University of Science and Technology of China（中国科学技术大学自动化系） ； School of Electronic Information Engineering, Beihang University（北京航空航天大学电子信息工程学院） ； School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院） ； National Superior College for Engineers, Beihang University（北京航空航天大学国家级工程师学院） ； Beijing Academy of Artificial Intelligence（北京人工智能研究院） ； Lenovo Group Limited（联想集团有限公司） ； Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院） 纠错

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

AI总结 本文通过协同VLM和纯视觉骨干网络，提出HybridDriveVLA和DualDriveVLA，提升端到端驾驶的PDMS性能。

An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving

端到端的决策感知多尺度注意力模型用于可解释的自动驾驶

Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi, Amir Abbas Hamidi Imani, Shahin Atakishiyev, Randy Goebel

机构 * Department of Computing Science, University of Alberta（阿尔伯塔大学计算机科学系） 纠错

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

AI总结 本文提出一种端到端的多尺度注意力模型，通过将驾驶决策输入推理组件，提供特定案例的解释。采用F1分数和新的联合F1分数评估模型性能，验证了其在可解释人工智能中的准确性和可靠性。

BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving

BridgeSim: 揭示端到端自动驾驶中的OL-CL差距

Seth Z. Zhao, Luobin Wang, Hongwei Ruan, Yuxin Bao, Yilan Chen, Ziyang Leng, Abhijit Ravichandran, Honglin He, Zewei Zhou, Xu Han, Abhishek Peri, Zhiyu Huang, Pranav Desai, Henrik Christensen, Jiaqi Ma, Bolei Zhou

机构 * UCLA（加州大学洛杉矶分校） ； UCSD（加州大学圣地亚哥分校） ； Qualcomm（高通公司） 纠错

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

AI总结 本文揭示了OL策略在闭环部署中转移效果差的根本原因，提出Test-Time Adaptation框架以校正观测域偏移和状态-动作偏差，提升端到端自动驾驶性能。

Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots

迈向自主安全的最后-mile配送：基于AI增强的自动驾驶配送机器人

Eyad Shaklab, Areg Karapetyan, Arjun Sharma, Murad Mebrahtu, Mustofa Basri, Mohamed Nagy, Majid Khonji, Jorge Dias

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

AI总结 本文提出了一种基于AI增强的自动驾驶配送机器人系统，用于实现小型城市社区的自主安全最后-mile配送，通过整合优化和现实约束，提升配送效率与客户满意度。

Addressing Corner Cases in Autonomous Driving: A World Model-based Approach with Mixture of Experts and LLMs

Haicheng Liao, Bonan Wang, Junxian Yang, Chengyue Wang, Zhengbin He, Guohui Zhang, Chengzhong Xu, Zhenning Li

机构 * State Key Laboratory of Internet of Things for Smart City, University of Macau（物联网智能城市国家重点实验室，澳门大学） ； Department of Computer and Information Science, University of Macau（计算机与信息科学系，澳门大学） ； Department of Civil and Environmental Engineering, University of Macau（土木与环境工程系，澳门大学） ； Senseable City Lab, Massachusetts Institute of Technology（可感知城市实验室，麻省理工学院） ； Department of Civil and Environmental Engineering, University of Hawaii（土木与环境工程系，夏威夷大学） 纠错

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

Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving

Boris Ivanovic, Cristiano Saltori, Yurong You, Yan Wang, Wenjie Luo, Marco Pavone

机构 * NVIDIA Research（NVIDIA研究） 纠错

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

Comments 12 pages, 10 figures, 5 tables

Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving

Lingyu Xiao, Jiang-Jiang Liu, Sen Yang, Xiaofan Li, Xiaoqing Ye, Wankou Yang, Jingdong Wang

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

Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving

Haoyi Niu, Qimao Chen, Yingyue Li, Yi Zhang, Jianming Hu

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

Exploring the Limitations of Behavior Cloning for Autonomous Driving

Felipe Codevilla, Eder Santana, Antonio M. López, Adrien Gaidon

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

End-to-end Driving via Conditional Imitation Learning

Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, Alexey Dosovitskiy

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

Comments Published at the International Conference on Robotics and Automation (ICRA), 2018

MPC-based Imitation Learning for Safe and Human-like Autonomous Driving

Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars, Tong Duy Son

专题命中 端到端驾驶 ：autonomous driving(title,comments)；分类 cs.RO

Comments Accepted at the 1st Workshop on Safe Learning for Autonomous Driving (SL4AD), co-located with the 39th International Conference on Machine Learning (ICML 2022)

Driving on Registers

在寄存器上驾驶

Ellington Kirby, Alexandre Boulch, Yihong Xu, Yuan Yin, Gilles Puy, Éloi Zablocki, Andrei Bursuc, Spyros Gidaris, Renaud Marlet, Florent Bartoccioni, Anh-Quan Cao, Nermin Samet, Tuan-Hung VU, Matthieu Cord

机构 * LIGM, ENPC, IP Paris, UGE, CNRS（LIGM，ENPC，IP Paris，UGE，CNRS） ； Sorbonne Université, CNRS, ISIR（Sorbonne Université，CNRS，ISIR） 纠错

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

AI总结 DrivoR通过引入相机感知的寄存器标记，结合轻量级Transformer解码器，在保持准确性的同时高效实现端到端自动驾驶。

IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model

Anqing Jiang, Yu Gao, Yiru Wang, Zhigang Sun, Shuo Wang, Yuwen Heng, Hao Sun, Shichen Tang, Lijuan Zhu, Jinhao Chai, Jijun Wang, Zichong Gu, Hao Jiang, Li Sun

机构 * Tsinghua University（清华大学） 纠错

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

Comments 9 pagres, 2 figures

Neural World Models for Computer Vision

Anthony Hu

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

Comments PhD thesis

Before Parc Fermé: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving

在封闭停车场之前：面向自动驾驶高效具身大语言模型的强化学习时间剪枝

Luca Benfenati, Ali Azimi, Matteo Risso, Fabio Carapellese, Daniele Jahier Pagliari, Alessio Burrello

机构 * Department of Control and Computer Engineering（控制与计算机工程系） ； Department of Mechanical and Aerospace Engineering（机械与航空航天工程系） 纠错

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

AI总结 提出一种在强化学习过程中进行剪枝的策略BPF，通过任务特定监督和闭环反馈压缩具身大语言模型控制器，在自动驾驶控制管道中实现了更好的性能-内存-吞吐量权衡。

SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World

SafeDrive: 为稀疏世界中的端到端驾驶进行细粒度安全推理

Jungho Kim, Jiyong Oh, Seunghoon Yu, Hongjae Shin, Donghyuk Kwak, Jun Won Choi

机构 * Seoul National University（首尔大学） 纠错

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

AI总结 SafeDrive提出了一种端到端规划框架，通过轨迹条件化的稀疏世界模型进行显式且可解释的安全推理，实现了在开放循环和闭合循环基准上的最佳性能，有效降低了碰撞率。

Comments Accepted to CVPR 2026, 19 pages, 9 figures

SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving

SparseDriveV2：仅需评分即可实现端到端自动驾驶

Wenchao Sun, Xuewu Lin, Keyu Chen, Zixiang Pei, Xiang Li, Yining Shi, Sifa Zheng

机构 * Tsinghua University（清华大学） ； Horizon Continental Technology（地平线大陆科技） ； Horizon（地平线） 纠错

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

AI总结 本文提出SparseDriveV2，通过可扩展的词汇表示和评分策略提升端到端自动驾驶性能，实现在NAVSIM和Bench2Drive上的高评分和成功率。

DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning

Wenru Liu, Pei Liu, Jun Ma

机构 * The Hong Kong University of Science and Technology（香港科技大学） 纠错

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

SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving

Yiming Xie, Henglu Wei, Zhenyi Liu, Xiaoyu Wang, Xiangyang Ji

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

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz

专题命中 端到端驾驶 ：occupancy(title)；分类 cs.CV

Journal ref Eur. Phys. J. C 82, 753 (2022)

Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation

Renhao Wang, Adam Scibior, Frank Wood

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

Comments Architecture fails to sufficiently disentangle representations and obey varied commands

Building a Winning Self-Driving Car in Six Months

Keenan Burnett, Andreas Schimpe, Sepehr Samavi, Mona Gridseth, Chengzhi Winston Liu, Qiyang Li, Zachary Kroeze, Angela P. Schoellig

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

Comments This work has been submitted to ICRA 2019

3D and 4D World Modeling: A Survey

3D和4D世界建模：一项综述

Lingdong Kong, Yu Yang, Jianbiao Mei, Youquan Liu, Ao Liang, Dekai Zhu, Dongyue Lu, Wei Yin, Xiaotao Hu, Mingkai Jia, Junyuan Deng, Kaiwen Zhang, Yang Wu, Tianyi Yan, Shenyuan Gao, Song Wang, Linfeng Li, Liang Pan, Yong Liu, Jianke Zhu, Wei Tsang Ooi, Steven C. H. Hoi, Ziwei Liu

机构 * WorldBench Team（WorldBench团队） 纠错

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

AI总结 该综述针对世界建模中对3D和4D表示利用不足及定义分类缺失的问题，通过建立精确概念、引入结构化分类法，系统总结相关数据集和评估指标，讨论应用、挑战与方向，为3D和4D世界建模领域提供基础参考。

Comments Survey; Project Page at https://worldbench.github.io/survey GitHub Repo at https://github.com/worldbench/awesome-3d-4d-world-models