自动驾驶

V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

Junwei You, Haotian Shi, Zhuoyu Jiang, Zilin Huang, Rui Gan, Keshu Wu, Xi Cheng, Xiaopeng Li, Bin Ran

机构 * organization= Department of Civil ； Environmental Engineering, University of Wisconsin–Madison , city= Madison , state= WI , postcode= 53706 , country= USA ； organization= College of Computing ； Data Science, Nanyang Technological University , city= Singapore , postcode= 639798 , country= Singapore ； organization= College of Transportation, Tongji University , city= Shanghai , postcode= 201804 , country= China ； organization= Zachry Department of Civil ； Environmental Engineering, Texas A\&M University , city= College Station , state= TX , postcode= 77840 , country= USA ； organization= School of Civil ； Environmental Engineering, Cornell University , city= Ithaca , state= NY , postcode= 14853 , country= USA 纠错

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

Ego-centric Learning of Communicative World Models for Autonomous Driving

Hang Wang, Dechen Gao, Junshan Zhang

机构 * Department of Electrical and Computer Engineering, University of California, Davis（电气与计算机工程系，加州大学戴维斯分校） 纠错

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

Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

Shahin Atakishiyev, Mohammad Salameh, Randy Goebel

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

Comments Accepted for publication in IEEE Transactions on Intelligent Transportation Systems

iPad: Iterative Proposal-centric End-to-End Autonomous Driving

Ke Guo, Haochen Liu, Xiaojun Wu, Jia Pan, Chen Lv

机构 * Nanyang Technological University（南洋理工大学） ； The University of Hong Kong（香港大学） ； Desay SV Automotive（Desay SV汽车） 纠错

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

RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations

Ruidan Xing, Runyi Huang, Qing Xu, Lei He

机构 * School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动系统学院） ； State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与移动系统国家重点实验室） ； School of Instrumentation and Optoelectronic Engineering, BeiHang University（北航仪器与光电工程学院） ； Department of Automation, Tsinghua University（清华大学自动化系） 纠错

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

From Imitation to Exploration: End-to-end Autonomous Driving based on World Model

Yueyuan Li, Mingyang Jiang, Songan Zhang, Wei Yuan, Chunxiang Wang, Ming Yang

机构 * Department of Automation, Shanghai Jiao Tong University（自动化系，上海交通大学） ； Key Laboratory of System Control and Information Processing, Ministry of Education of China（系统控制与信息处理重点实验室，中华人民共和国教育部） ； Global Institute of Future Technology, Shanghai Jiao Tong University（未来技术全球研究院，上海交通大学） 纠错

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

Comments 12 pages, 4 figures, 3 tables; T-ITS under review

VDT-Auto: End-to-end Autonomous Driving with VLM-Guided Diffusion Transformers

Ziang Guo, Konstantin Gubernatorov, Selamawit Asfaw, Zakhar Yagudin, Dzmitry Tsetserukou

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

Comments Submitted paper

End-to-End Autonomous Driving through V2X Cooperation

Haibao Yu, Wenxian Yang, Jiaru Zhong, Zhenwei Yang, Siqi Fan, Ping Luo, Zaiqing Nie

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

Comments Accepted by AAAI 2025. Add more open-loop evaluation indicators

Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving

Xiaosong Jia, Zhenjie Yang, Qifeng Li, Zhiyuan Zhang, Junchi Yan

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

Comments Accepted by NeurIPS 2024 Datasets and Benchmarks Track. Official Repo: https://github.com/Thinklab-SJTU/Bench2Drive

Guiding Attention in End-to-End Driving Models

Diego Porres, Yi Xiao, Gabriel Villalonga, Alexandre Levy, Antonio M. López

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

Comments Accepted for publication at the 35th IEEE Intelligent Vehicles Symposium (IV 2024)

Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs

Yiqun Duan, Qiang Zhang, Renjing Xu

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

Journal ref Published as oral presentation paper atthe 2024 IEEE International Conference on Robotics and Automation (ICRA2024), Yokohama, Japan

Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving

Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao

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

End-to-end Autonomous Driving using Deep Learning: A Systematic Review

Apoorv Singh

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

Comments 11 pages, 6 figures, submitted in WACV conference

Explaining Autonomous Driving Actions with Visual Question Answering

Shahin Atakishiyev, Mohammad Salameh, Housam Babiker, Randy Goebel

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

Comments Accepted to the 2023 IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC-2023)

Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments

Gustavo Claudio Karl Couto, Eric Aislan Antonelo

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

Journal ref 2021 IEEE Symposium Series on Computational Intelligence (SSCI)

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu

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

Journal ref CVPR 2022

MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen, Ren Xin, Lujia Wang

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

Comments 7 pages, 5 figures, accepted by IROS 2022

Efficient and Robust LiDAR-Based End-to-End Navigation

Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela Rus

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

Comments ICRA 2021. The first two authors contributed equally to this work. Project page: https://le2ed.mit.edu/

A Survey of End-to-End Driving: Architectures and Training Methods

Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman, Tambet Matiisen

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

Journal ref IEEE Transactions on Neural Networks and Learning Systems, 2020

Action-Based Representation Learning for Autonomous Driving

Yi Xiao, Felipe Codevilla, Christopher Pal, Antonio M. Lopez

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

Comments This paper has been accepted to the Conference on Robot Learning (CoRL 2020)

DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving

Yao Zhou, Guowei Wan, Shenhua Hou, Li Yu, Gang Wang, Xiaofei Rui, Shiyu Song

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

Comments 19 pages, 4 figures, Accepted by ECCV 2020

Towards Viewpoint-Robust End-to-End Autonomous Driving with 3D Foundation Model Priors

面向视角鲁棒的端到端自动驾驶与3D基础模型先验

Hiroki Hashimoto, Hiromichi Goto, Hiroyuki Sugai, Hiroshi Kera, Kazuhiko Kawamoto

机构 * Chiba University（千叶大学） ； National Institute of Informatics（国立信息学研究所） 纠错

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

AI总结 本文提出一种无需数据增强的端到端自动驾驶方法，利用3D基础模型的几何先验提升视角变化鲁棒性，实验显示在pitch和高度扰动下性能提升显著。

Comments Accepted at CVPR Workshop on Simulation for Autonomous Driving 2026

4D-WAM: 4D Consistent World Modeling for Autonomous Driving

4D-WAM：面向自动驾驶的4D一致世界建模

Jiacheng Fu, Yibo Yuan, Meng Tian, Yue Li, Jiangtong Zhu, Jianhua Han, Yueyi Zhang, Jianwu Fang, Jianru Xue, Hang Xu, Zhiwei Xiong

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

AI总结 本文提出4D-WAM模型，通过几何基础模型的训练时监督与面向决策的时间步长采样策略，提升自动驾驶中世界-动作模型的4D场景一致性，在NAVSIM-v1、NAVSIM-v2基准上实现最优性能。

UnsDrive: Towards Robust End-to-End Autonomous Driving in Unstructured Scenes

UnsDrive：面向非结构化场景的鲁棒端到端自动驾驶

Nanxin Zeng, Ruiqi Song, Xiangyu Guo, Baiyong Ding, Yunfeng Ai

机构 * University of Chinese Academy of Sciences（中国科学院大学） ； Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所） ； Waytous Inc.（文远知行公司） 纠错

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

AI总结 针对非结构化采矿环境自动驾驶泛化差的问题，提出UnsDrive规划器，结合未知感知占用表示与流匹配规划器，引入专用损失和评分器，辅以MineLoop模拟器验证，性能优于基线。

Comments 9 pages, 4 figures, conference

Journal ref the 34th ACM International Conference on Multimedia, 2026

VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models

VISA: VLM引导的实例语义审计用于3D占据世界模型

Ruiqi Xian, Yuehan Xian, Jing Liang, Xuewei Qi, Dinesh Manocha

机构 * University of Maryland College Park（马里兰大学帕克分校） ； Nanjing University of Posts and Telecommunications（南京邮电大学） ； Stanford University（斯坦福大学） ； Motional AD Inc.（Motional AD公司） 纠错

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

AI总结 提出VISA方法，利用离线VLM对每个物理对象实例进行结构化语义审计，并通过可靠性加权损失蒸馏到3D占据模型中，无需VLM推理即可提升封闭集占据mIoU。

SUV: Future Scene Understanding as Video Generation for End-to-End Driving

SUV：将未来场景理解建模为视频生成的端到端驾驶

Yibo Yuan, Jiacheng Fu, Jiangtong Zhu, Yi Li, Jianhua Han, Meng Tian, Zhuohan Liu, Zhiwei Xiong, Hang Xu, Jianwu Fang, Jianru Xue

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

AI总结 SUV是将未来场景理解建模为视频生成的端到端驾驶框架，其在NAVSIM-v2和WOD-E2E基准上优于近期SOTA方法，实现了更优的轨迹规划性能。

Comments 16 pages, 5 figures. Code: https://github.com/ASH-2046/SUV

MOJITO: Modal Joint Learning for Unified End-to-End Autonomous Driving

MOJITO：用于统一端到端自动驾驶的模态联合学习

Zhijing Cheng, Xuancheng Zhang, Donglin Di, Lei Fan, Baorui Ma, Hao Li, Xun Yang

机构 * University of Science and Technology of China（中国科学技术大学） ； Li Auto（理想汽车） ； University of New South Wales（新南威尔士大学） 纠错

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

AI总结 研究针对端到端自动驾驶系统问题，提出基于模态联合学习的MOJITO框架，去除级联接口，执行逐块模态联合注意力更新多模态特征，在数据集上取得新的最优成绩，展现出强大能力。

CascadeOcc: Rethinking 3D Occupancy World Models with Cascaded VQ Representations

CascadeOcc: 用级联VQ表示重新思考3D占用世界模型

Kyumin Hwang, Wonhyeok Choi, Jaeyeul Kim, Jihun Park, Daehee Park, Sunghoon Im

机构 * Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院） 纠错

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

AI总结 提出CascadeOcc，一种通过级联向量量化机制在自回归框架中利用占用表示内在结构层次，实现从粗到细的3D场景预测和运动规划，在4D占用预测和运动规划基准上取得优越性能。

Comments Accepted to IEEE Signal Processing Letters (SPL), 2026

SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation

SparseWorld: 通过具有稀疏场景表示的世界模型增强端到端自动驾驶

Ruoyu Wang, Jingke Wang, Yukai Ma, Yuehao Huang, Shuangming Lei, Guanglin Xu, Aixue Ye, Yong Liu

机构 * Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制系统研究所） ； Labs, Huawei（华为2012实验室） ； State Key Laboratory of Industrial Control Technology（国家工业控制技术重点实验室） 纠错

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

AI总结 提出SparseWorld，一种基于稀疏场景表示的轻量级世界模型，通过自回归预测未来地图元素和周围智能体，并利用预测结果优化下游运动预测和轨迹规划，在nuScenes数据集上实现0.05%的碰撞率，达到开放循环规划指标的最优性能。

Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving

Lagrange: 一种面向通用端到端驾驶的开放词汇、基于能量的稀疏框架

Shihao Ji, HongXi Li, Zihui Song, Mingyu Li

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

AI总结 提出Lagrange框架，利用掩码潜在场和视觉语言模型实现开放词汇、稀疏计算，通过拉格朗日动作最小化确保运动学约束，在nuScenes和CODA基准上验证了鲁棒性和可解释性。