自动驾驶

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

LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset

长尾驾驶场景与推理轨迹：KITScenes长尾数据集

Royden Wagner, Omer Sahin Tas, Jaime Villa, Felix Hauser, Yinzhe Shen, Marlon Steiner, Dominik Strutz, Carlos Fernandez, Christian Kinzig, Guillermo S. Guitierrez-Cabello, Hendrik Königshof, Fabian Immel, Richard Schwarzkopf, Nils Alexander Rack, Kevin Rösch, Kaiwen Wang, Jan-Hendrik Pauls, Martin Lauer, Igor Gilitschenski, Holger Caesar, Christoph Stiller

机构 * Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院） ； FZI Research Center for Information Technology（FZI信息技术研究中心） ； University Charles III of Madrid（马德里卡洛斯三世大学） ； Technical University of Madrid（马德里理工大学） ； University of Toronto（多伦多大学） ； Delft University of Technology（代尔夫特理工大学） 纠错

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

AI总结 本文提出KITScenes长尾数据集，通过多视角视频、轨迹、指令和推理轨迹提升模型在长尾驾驶场景下的泛化能力，评估指令遵循与语义连贯性。

Comments 21 pages; v2: update MMS values (bugfix)

WorldVLM: Combining World Model Forecasting and Vision-Language Reasoning

WorldVLM：结合世界模型预测与视觉语言推理

Stefan Englmeier, Katharina Winter, Fabian B. Flohr

机构 * Munich University of Applied Sciences（慕尼黑应用科学大学） 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

AI总结 WorldVLM结合视觉语言模型与世界模型，通过统一架构提升自动驾驶中的环境预测与决策能力，解决空间理解受限问题。

Comments 8 pages, 6 figures, 5 tables; submitted to IEEE

UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning

UniDWM: 通过多维表征学习实现统一的驾驶世界模型

Shuai Liu, Siheng Ren, Xiaoyao Zhu, Quanmin Liang, Zefeng Li, Qiang Li, Xin Hu, Kai Huang

机构 * Department of XXX, University of YYY, Location, Country（XXX系，YYY大学，地点，国家） ； School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家） ； Xpeng Motors Technology Co Ltd（小鹏汽车科技有限公司） 纠错

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

AI总结 UniDWM通过多维表征学习实现统一驾驶世界模型，提升自动驾驶中的轨迹规划和4D重建能力。

Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latent Space

Jian Zhu, Zhengyu Jia, Tian Gao, Jiaxin Deng, Shidi Li, Lang Zhang, Fu Liu, Peng Jia, Xianpeng Lang

机构 * Project Leader（项目负责人） 纠错

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

Comments 8 pages, 7 figures

Probing Multimodal LLMs as World Models for Driving

Shiva Sreeram, Tsun-Hsuan Wang, Alaa Maalouf, Guy Rosman, Sertac Karaman, Daniela Rus

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

Comments https://github.com/sreeramsa/DriveSim https://www.youtube.com/watch?v=Fs8jgngOJzU

Controlling Steering with Energy-Based Models

Mikita Balesni, Ardi Tampuu, Tambet Matiisen

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

Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models

Minting Pan, Xiangming Zhu, Yunbo Wang, Xiaokang Yang

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

End-to-end Interpretable Neural Motion Planner

Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun

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

Comments CVPR 2019 (Oral)

Driving Policy Transfer via Modularity and Abstraction

Matthias Müller, Alexey Dosovitskiy, Bernard Ghanem, Vladlen Koltun

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

Comments Accepted at Conference on Robotic Learning (CoRL'18) http://proceedings.mlr.press/v87/mueller18a.html

A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle

Michael Teti, William Edward Hahn, Shawn Martin, Christopher Teti, Elan Barenholtz

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

Comments 16 pages, 14 figures, 2 tables

Can the Cloud Drive? Infrastructure Feasibility of Offloading Autonomous Driving Across 5G and 6G

云能驱动自动驾驶吗？跨5G和6G卸载自动驾驶的基础设施可行性

Pouya Parsa, Kawon Han, Seongjin Choi

专题命中 端到端驾驶 ：autonomous driving(title)

AI总结 探讨云能否驱动自动驾驶，通过耦合通信限制、GPU服务模型等构建分析框架，应用于纽约市，研究发现通信、计算、成本依次起约束作用，延迟决定模型可行性，成本决定是否经济。

Comments 25 pages, 10 figures

DeepEye: A Steerable Self-driving Data Agent System

DeepEye：一种可调节的自动驾驶数据代理系统

Boyan Li, Yiran Peng, Yupeng Xie, Sirong Lu, Yizhang Zhu, Xing Mu, Xinyu Liu, Yuyu Luo

专题命中 端到端驾驶 ：self-driving(title)

AI总结 本文提出DeepEye，一种基于工作流的生产级数据代理系统，通过统一多模态协调协议和分层推理机制，解决异构数据源联合分析和上下文爆炸问题，实现透明执行和自动化优化。

Comments SIGMOD Demo (2026)

Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Guoyue Xu, Renzheng Zhang, Tengfei Luo

机构 * Self-Driving Laboratory（自主实验室） 纠错

专题命中 端到端驾驶 ：self-driving(title)

Autonomous Driving From the Sky: Design and End-to-End Performance Evaluation

Matteo Bordin, Marco Giordani, Michele Polese, Tommaso Melodia, Michele Zorzi

专题命中 端到端驾驶 ：autonomous driving(title)

OmniNWM: Omniscient Driving Navigation World Models

OmniNWM：全知驾驶导航世界模型

Bohan Li, Zhuang Ma, Dalong Du, Baorui Peng, Zhujin Liang, Zhenqiang Liu, Xianda Guo, Zheng Zhu, Chao Ma, Yueming Jin, Xin Jin, Hao Zhao, Wenjun Zeng

机构 * Shanghai Jiao Tong University（上海交通大学） ； Eastern Institute of Technology（东部技术研究所） ； PhiGent ； National University of Singapore（新加坡国立大学） ； Tsinghua University（清华大学） ； Wuhan University（武汉大学） 纠错

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

AI总结 提出OmniNWM全知全景导航世界模型，在统一概率框架下处理状态、动作和奖励三个维度，实现多模态全景视频生成、零样本轨迹控制及内在奖励机制，在生成保真度和控制精度上达到SOTA。

Comments ECCV 2026

Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation

自主智能体导航中学习非线性模型预测控制的贝叶斯优化

Lorenzo Ortolani, Gabriel Voss, Gabriele Beltrami, Francesco Dorati, Tommaso Felice Banfi

机构 * Talos Robotics AI 纠错

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

AI总结 提出一种无地图框架，结合滚动时域规划与非线性MPC，利用贝叶斯优化自动调参，在仿真和实物四足机器人上实现高效导航。

Comments Published at the IEEE ICRA 2026 Xplore Workshop (Oral), Cross-Disciplinary aspects of Exploration in Robotics, Reinforcement Learning, and Search

EponaV2: Driving World Model with Comprehensive Future Reasoning

EponaV2：通过全面的未来推理驱动世界模型

Jiawei Xu, Zhizhou Zhong, Zhijian Shu, Mingkai Jia, Mingxiao Li, Jia-Wang Bian, Qian Zhang, Kaicheng Zhang, Jin Xie, Jian Yang, Wei Yin

机构 * PCA Lab, VCIP, College of Computer Science, Nankai University（PCA实验室、VCIP、计算机科学学院、南开大学） ； Horizon Robotics ； HKUST（香港科技大学） ； NJUPT（南京工程大学） ； NTU（国立台湾大学） ； Anyverse ； School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院、南京大学） 纠错

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

AI总结 本文提出EponaV2，一种新的驾驶世界模型范式，通过全面的未来推理实现高质量规划。模型通过预测更全面的未来表示，结合3D和语义模态，提升环境理解与现实推理能力，从而改进轨迹规划。

End2Race: Efficient End-to-End Imitation Learning for Real-Time F1Tenth Racing

Zhijie Qiao, Haowei Li, Zhong Cao, Henry X. Liu

机构 * 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） 纠错

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

End-to-End Generation of City-Scale Vectorized Maps by Crowdsourced Vehicles

Zebang Feng, Miao Fan, Bao Liu, Shengtong Xu, Haoyi Xiong

机构 * NavInfo Co., Ltd.（NavInfo公司） ； Beijing Institute of Graphic Communication（北京图形通信学院） ； Autohome Inc.（Autohome公司） 纠错

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

Comments Accepted by ITSC'25

ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos

Xiaodong Wang, Peixi Peng

机构 * Peking University（北京大学） ； Peng Cheng Laboratory（鹏城实验室） 纠错

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

Comments 9 pages, 7 figures

Knowledge Graphs as World Models for Semantic Material-Aware Obstacle Handling in Autonomous Vehicles

Ayush Bheemaiah, Seungyong Yang

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

Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environments

Lu Yue, Dongliang Zhou, Liang Xie, Feitian Zhang, Ye Yan, Erwei Yin

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

Journal ref IEEE Robotics and Automation Letters, 2024

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

Leandro Parada, Hanlin Tian, Jose Escribano, Panagiotis Angeloudis

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

Journal ref Journal reference: 2023 IEEE 26th International Conference on Intelligent Transportation Systems, pp. 5548-5554, 2023

DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Xinze Chen, Guan Huang, Xiaoyi Bao, Xingang Wang

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

Comments Project Page: https://drivedreamer2.github.io

Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognition

Chen Yan, Zhijian Xu, Zhanyuan Yin, Xiaoyu Ji, Wenyuan Xu

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

Comments To be published in USENIX Security 2022