自动驾驶 - arXivDaily 专题

2210.17302 2022-11-08 cs.RO cs.SY eess.SY 79%

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Daegyu Lee, Hyunki Seong, Seungil Han, Gyuree Kang, D. Hyunchul Shim, Yoonjin Yoon

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

2210.14721 2022-10-27 cs.LG cs.AI 79%

Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data

John So, Amber Xie, Sunggoo Jung, Jeffrey Edlund, Rohan Thakker, Ali Agha-mohammadi, Pieter Abbeel, Stephen James

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

Comments CoRL 2022 Paper

2205.12124 2022-05-25 cs.RO 79%

Memory based neural networks for end-to-end autonomous driving

Sergio Paniego Blanco, Sakshay Mahna, Utkarsh A. Mishra, JoseMaria Canas

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

Comments 6 pages, 3 figures, Code available: https://github.com/JdeRobot/BehaviorMetrics and https://www.github.com/JdeRobot/DeepLearningStudio

2205.07481 2022-05-17 cs.RO 79%

Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving

Unnikrishnan R Nair, Sarthak Sharma, Udit Singh Parihar, Midhun S Menon, Srikanth Vidapanakal

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

2110.01050 2021-10-05 stat.ML cs.CV cs.LG 79%

Marginally calibrated response distributions for end-to-end learning in autonomous driving

Clara Hoffmann, Nadja Klein

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

Comments 17 pages, 9 figures

2104.01093 2021-04-09 cs.RO 79%

SMET: Scenario-based Metamorphic Testing for Autonomous Driving Models

Haiyang Ao, Ya Pan

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

Comments This article has been withdrawn by arXiv administrators due to excessive verbatim text overlap from external sources

2006.03347 2020-06-08 cs.CV 79%

Explaining Autonomous Driving by Learning End-to-End Visual Attention

Luca Cultrera, Lorenzo Seidenari, Federico Becattini, Pietro Pala, Alberto Del Bimbo

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

Comments accepted at CVPR20 Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD20)

1905.00689 2019-10-31 eess.SP cs.LG cs.RO 79%

Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars

Alexandros Kouris, Stylianos I. Venieris, Michail Rizakis, Christos-Savvas Bouganis

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

Comments PREPRINT: Accepted for publication at the IEEE Consumer Electronics Magazine (CEM). [Acceptance Date: 28-Oct-2019]

1709.07174 2019-08-12 cs.RO 79%

Agile Autonomous Driving using End-to-End Deep Imitation Learning

Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos Theodorou, Byron Boots

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

Comments 13 pages, Robotics: Science and Systems (RSS) 2018

1903.05157 2019-03-14 cs.RO cs.LG stat.ML 79%

Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models

Adith Boloor, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang

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

1811.05785 2018-12-18 cs.LG cs.AI 79%

Two-stream convolutional networks for end-to-end learning of self-driving cars

Nelson Fernandez

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

Journal ref NeurIPS 2018 Workshop on modeling and decision-making in the spatiotemporal domain, Montreal, Canada

1811.06817 2018-11-19 cs.LG cs.CV stat.ML 79%

Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control

Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal

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

Comments 7 pages, 6 figures

1801.03458 2018-09-07 cs.CV 79%

Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

Luona Yang, Xiaodan Liang, Tairui Wang, Eric Xing

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

1806.11349 2018-07-02 cs.CV 79%

Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles

Rooz Mahdavian, Richard Diehl Martinez

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

1805.07545 2018-05-22 cs.CV 79%

End-to-end driving simulation via angle branched network

Qing Wang, Long Chen, Wei Tian

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

Comments 10 pages,6 figures

1712.04363 2018-04-04 cs.AI 79%

Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning

Patrick Klose, Rudolf Mester

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

Comments The paper is submitted to be included in the proceedings of Applications of Intelligent Systems 2018 (APPIS 2018)

2409.11356 2025-02-14 cs.CV cs.AI 79%

RenderWorld: World Model with Self-Supervised 3D Label

Ziyang Yan, Wenzhen Dong, Yihua Shao, Yuhang Lu, Liu Haiyang, Jingwen Liu, Haozhe Wang, Zhe Wang, Yan Wang, Fabio Remondino, Yuexin Ma

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

Comments Accepted in 2025 IEEE International Conference on Robotics and Automation (ICRA)

2607.15820 2026-07-20 cs.SE 新提交 78%

In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing

掌控全局：关于自动驾驶系统测试现状的多公司研究

Qunying Song, Yuan Gao, Johannes Betz, Dietmar Pfahl, Mohammad Reza Mousavi, Federica Sarro

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

AI总结针对自动驾驶系统测试复杂且缺乏标准的问题，通过对九家公司专家访谈，经主题分析总结行业测试情况、挑战与趋势，提出以证据为中心的闭环测试框架，为ADS测试提供指导并指明未来方向。

Comments 34 pages, 6 figures, 3 tables

2606.10303 2026-06-10 cs.AR cs.DC 新提交 78%

Isolation-aware Scheduling Framework for DNN-based End-to-End Autonomous Driving System on Tile-based Accelerators

基于瓦片加速器的端到端自动驾驶系统中DNN的隔离感知调度框架

Chenguang Zhang, Yuanpeng Zhang, Chenhao Xue, Yihan Yin, Chen Zhang, Guangyu Sun

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

AI总结提出ADS-Tile框架，通过可配置隔离与弹性预留结合，在瓦片加速器上优化DNN共置调度，降低重分配开销，满足严格延迟约束。

Comments Accepted by IEEE Transactions on Computers

2605.17268 2026-05-26 cs.AI cs.CV cs.RO 78%

Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models

VLA 推理是否忠实？自动驾驶模型中因果链的安全性探究

Nicanor Mayumu, Xiaoheng Deng, Patrick Mukala

机构 * School of Computer Science and Engineering（计算机科学与工程学院）； Central South University（中南大学）； School of Computer Science（计算机科学学院）； University of Wollongong in Dubai（迪拜大学）

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

AI总结通过分析300次VLA推理，发现输出推理与轨迹的忠实度仅42.5%，存在大量漏检行人、轨迹脆弱及推理-动作不一致问题，并提出了信息论忠实度形式化定义与安全架构。

Comments Accept (Poster), CVPR 2026 Workshop DriveX NonArchival Track

2605.00531 2026-05-04 cs.SE 78%

From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry

从研究到实践：一项与行业从业者互动的快速审查，探讨自动驾驶系统测试

Qunying Song, Ali Nouri, Håkan Sivencrona, Federica Sarro

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

AI总结本文通过与21名汽车行业从业者的互动快速审查，揭示了自动驾驶系统测试中的关键挑战，特别是端到端系统测试的完整性和方法，为行业提供实践洞察。

Comments 12 pages, 1 figure, 10 tables

2604.23362 2026-04-28 cs.SE cs.LG 78%

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems

UniAda：面向端到端自动驾驶系统的通用自适应多目标对抗攻击

Jingyu Zhang, Jacky Wai Keung, Yan Xiao, Yihan Liao, Yishu Li, Xiaoxue Ma

机构 * Department of Computer Science, City University of Hong Kong（城市大学计算机科学系）； School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区信息科学与技术学院）

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

AI总结 UniAda提出一种多目标白盒攻击方法，通过自适应加权方案优化，同时影响转向和速度控制，验证结果显示其在转向和速度偏差上优于现有方法。

Comments Published at IEEE Transactions on Reliability journal (2023)

2604.16436 2026-04-21 cs.NE cs.LG 78%

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving

模糊编码-解码以提升自动驾驶中的脉冲Q学习性能

Aref Ghoreishee, Abhishek Mishra, Lifeng Zhou, John Walsh, Anup Das, Nagarajan Kandasamy

机构 * Electrical and Computer Engineering Department（电气与计算机工程系）； Drexel University（德雷塞尔大学）

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

AI总结本文提出模糊编码-解码架构，通过改进视觉多模态深度脉冲Q网络，解决脉冲强化学习中的信息丢失和表征能力不足问题，提升自动驾驶决策精度。

2604.13139 2026-04-16 physics.ed-ph cond-mat.mtrl-sci 78%

Building an Affordable Self-Driving Lab: Practical Machine Learning Experiments for Physics Education Using Internet-of-Things

构建一个经济实惠的自动驾驶实验室：利用物联网进行物理教育中的实用机器学习实验

Yang Liu, Qianjie Lei, Xiaolong He, Yizhe Xue, Kexin He, Haitao Yang, Yong Wang, Xian Zhang, Li Yang, Yichun Zhou, Ruiqi Hu, Yong Xie

专题命中端到端驾驶：self-driving(title,abstract)

AI总结本文提出一个低成本的物联网实验平台，用于物理教育中的机器学习实践，展示深度学习在处理复杂非线性关系上的优势。

Journal ref APL Mach. Learn. 3, 046105 (2025)

2511.09025 2025-11-13 cs.LG 78%

FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks

Tianao Xiang, Mingjian Zhi, Yuanguo Bi, Lin Cai, Yuhao Chen

机构 * Northeastern University（东北大学）； University of Victoria（维多利亚大学）

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

2509.16950 2025-10-14 cs.CR 78%

Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving

Xuan Chen, Shiwei Feng, Zikang Xiong, Shengwei An, Yunshu Mao, Lu Yan, Guanhong Tao, Wenbo Guo, Xiangyu Zhang

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

2510.00092 2025-10-02 cs.SE 78%

A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0

Shufeng Chen, Mariat James Elizebeth, Robab Aghazadeh Chakherlou, Xingyu Zhao, Eric Barbier, Siddartha Khastgir, Paul Jennings

专题命中端到端驾驶：self-driving(title,abstract)

2505.06780 2025-05-14 cs.OS 78%

Work-in-Progress: Multi-Deadline DAG Scheduling Model for Autonomous Driving Systems

Atsushi Yano, Takuya Azumi

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

2503.11650 2025-03-17 cs.RO cs.AI cs.CV cs.LG 78%

Centaur: Robust End-to-End Autonomous Driving with Test-Time Training

Chonghao Sima, Kashyap Chitta, Zhiding Yu, Shiyi Lan, Ping Luo, Andreas Geiger, Hongyang Li, Jose M. Alvarez

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

2210.04017 2024-05-21 cs.LG 78%

Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model

Zeyu Gao, Yao Mu, Chen Chen, Jingliang Duan, Shengbo Eben Li, Ping Luo, Yanfeng Lu

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

Comments 13 pages, 13 figures, 4 tables, this version is accepted by IEEE Transactions on Intelligent Transportation Systems, the workshop version can be found in NeurIPS Deep RL Workshop 2022

1. 端到端驾驶 725 篇

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data

Memory based neural networks for end-to-end autonomous driving

Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving

Marginally calibrated response distributions for end-to-end learning in autonomous driving

SMET: Scenario-based Metamorphic Testing for Autonomous Driving Models

Explaining Autonomous Driving by Learning End-to-End Visual Attention

Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars

Agile Autonomous Driving using End-to-End Deep Imitation Learning

Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models

Two-stream convolutional networks for end-to-end learning of self-driving cars

Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control

Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles

End-to-end driving simulation via angle branched network

Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning

RenderWorld: World Model with Self-Supervised 3D Label

In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing

Isolation-aware Scheduling Framework for DNN-based End-to-End Autonomous Driving System on Tile-based Accelerators

Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models

From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems

Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving

Building an Affordable Self-Driving Lab: Practical Machine Learning Experiments for Physics Education Using Internet-of-Things

FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks

Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving

A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0

Work-in-Progress: Multi-Deadline DAG Scheduling Model for Autonomous Driving Systems

Centaur: Robust End-to-End Autonomous Driving with Test-Time Training

Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model