自动驾驶

Risk Scenario Generation for Autonomous Driving Systems based on Causal Bayesian Networks

Jiangnan Zhao, Dehui Du, Xing Yu, Hang Li

专题命中 仿真评测 ：autonomous driving(title,abstract)

Comments 10 pages

Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers?

Francesco Marchiori, Alessandro Brighente, Mauro Conti

专题命中 仿真评测 ：autonomous driving(title,abstract)

Comments Accepted at ACSW 2024

Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

Bo Yang, Xueyao Zhang, Zhiwen Yu, Xuelin Cao, Chongwen Huang, George C. Alexandropoulos, Yan Zhang, Merouane Debbah, Chau Yuen

专题命中 仿真评测 ：autonomous driving(title,abstract)

Diversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space Encoding

Timo Blattner, Christian Birchler, Timo Kehrer, Sebastiano Panichella

专题命中 仿真评测 ：self-driving(title,abstract)

Towards Automated Driving Violation Cause Analysis in Scenario-Based Testing for Autonomous Driving Systems

Ziwen Wan, Yuqi Huai, Yuntianyi Chen, Joshua Garcia, Qi Alfred Chen

专题命中 仿真评测 ：autonomous driving(title,abstract)

SensoDat: Simulation-based Sensor Dataset of Self-driving Cars

Christian Birchler, Cyrill Rohrbach, Timo Kehrer, Sebastiano Panichella

专题命中 仿真评测 ：self-driving(title,abstract)

A Critical Evaluation of a Self-Driving Laboratory for the Optimization of Electrodeposited Earth-Abundant Mixed-Metal Oxide Catalysts for the Oxygen Evolution Reaction (OER)

Erfan Fatehi, Manish Thadani, Gabriel Birsan, Robert W Black

专题命中 仿真评测 ：self-driving(title,abstract)

Comments 46 pages, 8 main body figures, 11 supporting information figures

Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)

Piyush Gupta, Demetris Coleman, Joshua E. Siegel

专题命中 仿真评测 ：self-driving(title,abstract)

BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems

Mingfei Cheng, Yuan Zhou, Xiaofei Xie

专题命中 仿真评测 ：autonomous driving(title,abstract)

TEASER: Simulation-based CAN Bus Regression Testing for Self-driving Cars Software

Christian Birchler, Cyrill Rohrbach, Hyeongkyun Kim, Alessio Gambi, Tianhai Liu, Jens Horneber, Timo Kehrer, Sebastiano Panichella

专题命中 仿真评测 ：self-driving(title,abstract)

Cost-effective Simulation-based Test Selection in Self-driving Cars Software

Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, Sebastiano Panichella

专题命中 仿真评测 ：self-driving(title,abstract)

Simulation-based Validation for Autonomous Driving Systems

Changwen Li, Joseph Sifakis, Qiang Wang, Rongjie Yan, Jian Zhang

专题命中 仿真评测 ：autonomous driving(title,abstract)

Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software

Christian Birchler, Sajad Khatiri, Bill Bosshard, Alessio Gambi, Sebastiano Panichella

专题命中 仿真评测 ：self-driving(title,abstract)

Comments arXiv admin note: substantial text overlap with arXiv:2111.04666

Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?

Guannan Lou, Yao Deng, Xi Zheng, Mengshi Zhang, Tianyi Zhang

专题命中 仿真评测 ：autonomous driving(title,abstract)

Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software

Sajad Khatiri, Christian Birchler, Bill Bosshard, Alessio Gambi, Sebastiano Panichella

专题命中 仿真评测 ：self-driving(title,abstract)

Comments 39 pages

Paracosm: A Language and Tool for Testing Autonomous Driving Systems

Rupak Majumdar, Aman Mathur, Marcus Pirron, Laura Stegner, Damien Zufferey

专题命中 仿真评测 ：autonomous driving(title,abstract)

Comments Technical Report to complement paper at FASE 2021

Communication and Computing Resource Optimization for Connected Autonomous Driving

Kai Xiong, Supeng Leng, Xiaosha Chen, Chongwen Huang, Chau Yuen, Yong Liang Guan

专题命中 仿真评测 ：autonomous driving(title,abstract)

Comments 12 pages, 7 figures

A machine learning environment for evaluating autonomous driving software

Jussi Hanhirova, Anton Debner, Matias Hyyppä, Vesa Hirvisalo

专题命中 仿真评测 ：autonomous driving(title,abstract)

Comments 8 pages, 13 figures

Journal ref Embedded World Conference 2019 Proceedings

Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving

Weizi Li

专题命中 仿真评测 ：autonomous driving(title,abstract)

Comments PhD Thesis, Department of Computer Science, The University of North Carolina at Chapel Hill, July 2019

A Case Study of Trust on Autonomous Driving

Shili Sheng, Erfan Pakdamanian, Kyungtae Han, BaekGyu Kim, Prashant Tiwari, Inki Kim, Lu Feng

专题命中 仿真评测 ：autonomous driving(title,abstract)

On Analyzing Self-Driving Networks: A Systems Thinking Approach

Touseef Yaqoob, Muhammad Usama, Junaid Qadir, Gareth Tyson

专题命中 仿真评测 ：self-driving(title,abstract)

Systematic Evaluation of Sandboxed Software Deployment for Real-time Software on the Example of a Self-Driving Heavy Vehicle

Philip Masek, Magnus Thulin, Hugo Andrade, Christian Berger, Ola Benderius

专题命中 仿真评测 ：self-driving(title,abstract)

Comments Copyright 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Controllable Radar Simulation with Waveform Parameter Embedding

具有波形参数嵌入的可控雷达仿真

Weiqing Xiao, Hao Huang, Chonghao Zhong, Yujie Lin, Nan Wang, Xiaoxue Chen, Zhaoxi Chen, Saining Zhang, Shuocheng Yang, Pierre Merriaux, Lei Lei, Hao Zhao

机构 * NJU（南京大学） ； BJTU（北京理工大学） ； BIT（北京理工大学） ； AIR, THU（空气科技，清华大学） ； NTU（国立台湾大学） ； SVM, THU（SVM，清华大学） ； Lightwheel AI ； LeddarTech 纠错

专题命中 仿真评测 ：LiDAR(abstract,abstract_cn)；autonomous driving(abstract)；分类 cs.CV

AI总结 提出Ctrl-RS框架，通过环境反射张量、波形参数抽象和WARP-Net网络，实现可控的雷达立方体仿真，在2D/3D检测和语义分割任务中性能接近或超越真实雷达。

Comments CVPR 2026 Findings: Code: https://github.com/zhuxing0/SA-Radar Project page: https://zhuxing0.github.io/projects/SA-Radar

UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition

UniD-Shift：通过可解释的共享-私有多模态分解实现统一的语义分割

Shuai Zhang, Zhecheng Shi, Zhuxiao Li, Jing Ou, Tengxi Wang, Yuan Liu, Wufan Zhao

机构 * HKUST(GZ)（香港科技大学（广州）） 纠错

专题命中 仿真评测 ：LiDAR(abstract,abstract_cn)；autonomous driving(abstract)；分类 cs.CV

AI总结 本文提出UniD-Shift框架，通过结合视觉和几何编码器提取互补特征，并通过共享-私有子空间分解实现2D-3D语义分割的统一。实验表明在SemanticKITTI和nuScenes数据集上优于基线方法，且具有良好的泛化能力。

OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective

OccuFly：一种用于从空中视角进行语义场景补全的3D视觉基准

Markus Gross, Sai B. Matha, Aya Fahmy, Rui Song, Daniel Cremers, Henri Meess

机构 * Fraunhofer Institute IVI（弗劳恩霍夫交通与基础设施系统研究所） ； TU Munich（慕尼黑工业大学） ； MCML（慕尼黑机器学习中心） ； UCLA（加州大学洛杉矶分校） 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.CV

AI总结 本文提出OccuFly基准，通过无LiDAR相机数据生成框架，提供21类语义的20000+样本，评估视觉模型在空中场景中的局限性。

Comments Accepted to CVPR 2026

A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation

Jonathan Sadeghi, Blaine Rogers, James Gunn, Thomas Saunders, Sina Samangooei, Puneet Kumar Dokania, John Redford

专题命中 仿真评测 ：autonomous driving(abstract,comments)；LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments To appear in NeurIPS 2021 Workshop on Machine Learning for Autonomous Driving (ML4AD)

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning

CoopReflect：通过多智能体学习实现协同自动驾驶的自然语言通信

Jiaxun Cui, Chen Tang, Jarrett Holtz, Janice Nguyen, Alessandro G. Allievi, Hang Qiu, Peter Stone

机构 * The University of Texas at Austin（德克萨斯大学奥斯汀分校） ； Robert Bosch LLC（罗伯特·博世有限公司） ； University of California, Riverside（加州大学河滨分校） ； Sony AI（索尼人工智能） 纠错

专题命中 仿真评测 ：autonomous driving(title)；分类 cs.RO、cs.AI

AI总结 研究探索自然语言用于车对车通信的潜力，开发基于大语言模型的驾驶智能体，引入CoopReflect多智能体学习框架，通过试错等为智能体配备相关知识，实验表明其能生成更优消息，实现跨场景泛化并降低决策延迟。

Journal ref Proc. 25th Int. Conf. Autonomous Agents and Multiagent Systems (AAMAS), 744-752 (2026)

Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning

Shift & Drift：用于可泛化且稳健的自动驾驶运动规划的零样本基准测试

Alessandro Canevaro, Hang Yu, Julian Schmidt, Peizheng Li, Silvan Lindner, Wilhelm Stork, Georg Martius, Julian Jordan

机构 * Mercedes-Benz AG, Research & Development（梅赛德斯-奔驰股份公司，研发部） ； University of Tübingen（图宾根大学） ； Karlsruhe Institute of Technology, ITIV（卡尔斯鲁厄理工学院，ITIV） 纠错

专题命中 仿真评测 ：autonomous driving(title)；分类 cs.RO、cs.AI

AI总结 研究自动驾驶运动规划器在新城市拓扑泛化及执行扰动恢复方面的不足，提出Shift & Drift双轨基准测试，通过语义转移和状态分布漂移测试不同规划范式，发现模仿学习与强化学习在性能上的差异，为评估可靠部署提供基准。

Comments Accepted at 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)

Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing

用于可信自动驾驶系统测试的模拟器集成

Lev Sorokin, Matteo Biagiola, Andrea Stocco

机构 * Technical University of Munich（慕尼黑技术大学） ； University of St. Gallen（圣加尔登大学） 纠错

专题命中 仿真评测 ：autonomous driving(title)；分类 cs.RO、cs.AI

AI总结 研究自动驾驶系统测试中模拟器结果不一致问题，提出MultiSim方法，利用模拟器集成和搜索策略识别故障场景，通过实验证明该方法比单模拟器及现有多模拟器方法更优，能提高故障识别率，还提出预测分歧和利用代理模型提高效率。

Journal ref Published in Empirical Software Engineering (EMSE), Vol. 31, No. 4, Article 80 (2026)

ReSim: Reliable World Simulation for Autonomous Driving

ReSim：面向自动驾驶的可靠世界模拟

Jiazhi Yang, Kashyap Chitta, Shenyuan Gao, Long Chen, Yuqian Shao, Xiaosong Jia, Hongyang Li, Andreas Geiger, Xiangyu Yue, Li Chen

机构 * The Chinese University of Hong Kong（香港中文大学） ； The University of Hong Kong（香港大学） ； OpenDriveLab at Shanghai AI Lab（上海人工智能实验室OpenDrive实验室） ； NVIDIA Research（NVIDIA研究） ； Xiaomi EV（小米电动车） ； Shanghai Jiao Tong University（上海交通大学） ； University of Tübingen, Tübingen AI Center（图宾根大学，图宾根人工智能中心） ； HKUST（香港科技大学） 纠错

专题命中 仿真评测 ：autonomous driving(title)；分类 cs.RO、cs.CV

AI总结 本文提出ReSim模型，通过整合真实数据与模拟数据提升自动驾驶场景模拟的可靠性与可控性，提升规划和策略选择性能。

Comments NeurIPS 2025 Spotlight. Project page: https://opendrivelab.com/ReSim