自动驾驶

Toward the Right Analytical Model and System Software for Autonomous Driving Systems: Open Problems and Research Directions

迈向自动驾驶系统的正确分析模型和系统软件：开放问题与研究方向

Atsushi Yano, Takuya Azumi

专题命中 感知 ：autonomous driving(title,abstract)

AI总结 探讨自动驾驶系统中，需何种分析模型及系统软件来处理定时问题。通过调研相关工作，从定时约束单元等五个维度找出差距。

Journal ref RTSOPS 2026 invited paper

NOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving

NOVA：自动驾驶中3D多目标跟踪的下一步开放词汇自回归

Kai Luo, Xu Wang, Rui Fan, Kailun Yang

机构 * College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院） ； State Key Laboratory of Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统国家重点实验室） 纠错

专题命中 感知 ：autonomous driving(title)；分类 cs.RO、cs.CV、eess.IV

AI总结 提出NOVA方法，通过自回归关联将3D多目标跟踪转化为轨迹条件时空语义序列完成，利用大语言模型实现开放词汇泛化，在nuScenes等数据集上显著提升新类别跟踪性能。

Comments Accepted to IROS 2026. Code will be available at https://github.com/xifen523/NOVA

When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems

当停止失效：通过人机交互自动驾驶重新思考安全交通系统的最小风险条件

Yash Tandon, Giovanni Tapia Lopez, Marcus Blennemann, Mohan Trivedi, Ross Greer

专题命中 感知 ：autonomous driving(title)；分类 cs.RO、cs.CV、cs.AI

AI总结 分析自动驾驶车辆因停止行为导致的交通阻塞、应急干扰等事故，提出需从被动策略转向人机交互自主性，增强感知、规划与控制能力。

Comments 8 pages, 1 figure, Accepted to IEEE ITSC 2026

Towards Safety-Aware Mutation Testing for Autonomous Driving Systems

面向自动驾驶系统的安全感知变异测试

Donghwan Shin

专题命中 感知 ：autonomous driving(title,abstract)

AI总结 提出安全感知变异测试（SAMT），通过注入模块间消息故障模拟交互失效，基于STPA生成突变体，评估测试充分性并指导场景生成与系统修复。

Comments 6 pages; vision paper

CADENet: Condition-Adaptive Asynchronous Dual-Stream Enhancement Network for Adverse Weather Perception in Autonomous Driving

CADENet：条件自适应异步双流增强网络用于自动驾驶中的恶劣天气感知

Sherif Khairy, Catherine M. Elias

机构 * Computer Science & Engineering Department, German University in Cairo (GUC), Egypt（德国开罗大学（GUC）计算机科学与工程系，埃及） ； C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems, Cairo, Egypt（认知驾驶系统实验室（C-DRiVeS Lab），开罗，埃及） 纠错

专题命中 感知 ：autonomous driving(title)；分类 cs.RO、cs.CV、cs.AI

AI总结 本文提出CADENet，一种无需训练的三线系统，通过条件自适应增强和熵引导NMS融合，实现自动驾驶中恶劣天气下的目标检测，同时无需重新训练或额外硬件。

A Novel Segment-Based Tracking Algorithm for HLT under High-Occupancy and Complex Conditions

一种用于高占能和复杂条件下的新型基于段的跟踪算法

Pengkun Jia, Zhujun Fang, Hang Zhou, Yuhe Huang, Changqing Feng, Jianbei Liu

专题命中 感知 ：occupancy(title,abstract)

AI总结 本文提出一种基于跟踪段的新型HLT跟踪算法，通过优化边缘矩阵和合并立体超层段，减少存储段数和边缘矩阵大小，提升HLT跟踪性能，实现数据压缩比达50%-70%。

Lightweight Low-SNR-Robust Semantic Communication System for Autonomous Driving

轻量级低信噪比鲁棒语义通信系统用于自动驾驶

Ruixing Ren, Minjie Wei, Junhui Zhao

专题命中 感知 ：autonomous driving(title,abstract)

AI总结 本文提出一种轻量级低信噪比鲁棒语义通信系统，通过结构剪枝和统一量化与M-QAM调制方案，提升自动驾驶车辆间协作感知的图像传输性能。

Comments 9 pages, 6 figures

Generalizability of Learning-based Occupancy Detection in Residential Buildings

基于学习的住宅建筑占用检测的泛化能力

Mahsa Farjadnia, Katayoun Eshkofti, Albin Apell, Tilde Hjalmarsson, Karl Henrik Johansson, Angela Fontan, Marco Molinari

专题命中 感知 ：occupancy(title,abstract)

AI总结 本文评估了三种机器学习方法在住宅建筑中非侵入式占用检测中的性能，发现LSTM在跨房间泛化能力最强，LR则在低复杂度应用中表现良好。

Comments 8 pages, 8 Figures, 4 Tables, Accepted for publication in the proceedings of the 2026 IFAC World Congress, Busan, Republic of Korea

Foundation Models for Autonomous Driving System: An Initial Roadmap

自动驾驶系统中的基础模型：初步路线图

Xiongfei Wu, Mingfei Cheng, Xiaoning Ren, Qiang Hu, Jianlang Chen, Yuheng Huang, Maxime Cordy, Yao Zhang, Xiaofei Xie, Lei Ma, Yves Le Traon

专题命中 感知 ：autonomous driving(title,abstract)

AI总结 本文探讨了基础模型在自动驾驶系统中的应用，分析了在基础设施、车载集成和实际部署三个维度中的现状、挑战和研究方向，旨在为构建安全可靠的自动驾驶系统提供指导。

Comments To appear in ACM Transactions on Software Engineering and Methodology (TOSEM)

Multi-mode Coherent Detection Ghost Imaging Lidar and Vibration-Mode Imaging

多模式相干检测鬼成像雷达与振动模式成像

Jinquan Qi, Shuang Liu, Chenjin Deng, Chaoran Wang, Zunwang Bo, Youzhen Gui, Shensheng Han

专题命中 感知 ：LiDAR(title,abstract)

AI总结 本文提出了一种基于桶探测器的多模式相干检测方案，用于实现多模式光场的相干混合与振动模式重建，提升雷达的抗干扰和相位分辨能力。

Comments 16 pages, 7 figures

Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs

探索kNN噪声特征检测与恢复在自动驾驶实验室中的局限性

Qiuyu Shi, Kangming Li, Yao Fehlis, Runze Zhang, Daniel Persaud, Robert Black, Jason Hattrick-Simpers

专题命中 感知 ：self-driving(title,abstract)

AI总结 本研究探讨了kNN噪声特征检测与恢复在自动驾驶实验室中的局限性，通过系统分析噪声强度、数据集大小等因素对特征可检测性和可恢复性的影响，提出了一种通用的数据恢复框架并建立了材料数据集的基准。

Comments 20 pages, 7 figures

Doppler-Domain Respiratory Amplification for Semi-Static Human Occupancy Detection Using Low-Resolution SIMO FMCW Radar

多普勒域呼吸放大用于低分辨率SIMO FMCW雷达的半静态人体占用检测

Huy Trinh, Elliot Creager, George Shaker

专题命中 感知 ：occupancy(title,abstract)

AI总结 本研究提出RASSO方法，通过多普勒域呼吸放大提升低分辨率雷达半静态人体检测性能，实现高准确率和可靠性。

Reliable Quasi-Static Post-Fall Floor-Occupancy Detection Using Low-Cost Millimetre-Wave Radar

可靠的低成本毫米波雷达用于静态后跌倒地板占用检测

Huy Trinh, Phuong Thai, Elliot Creager, George Shaker

专题命中 感知 ：occupancy(title,abstract)

AI总结 本文提出了一种基于低成本毫米波雷达的可靠后跌倒地板占用检测方法，通过对比数字波束成形与MVDR波束成形，提高了检测精度。

RE for AI in Practice: Managing Data Annotation Requirements for AI Autonomous Driving Systems

在实践中为AI管理数据标注需求：为AI自主驾驶系统管理数据标注要求

Hina Saeeda, Mazen Mohamad, Eric Knauss, Jennifer Horkoff, Ali Nouri

专题命中 感知 ：autonomous driving(title,abstract)

AI总结 本研究通过实证分析，探讨了自动驾驶系统中数据标注需求的定义、挑战及最佳实践，为提升AI系统可靠性提供指导。

Towards the Detection of Building Occupancy with Synthetic Environmental Data

Manuel Weber, Christoph Doblander, Peter Mandl

机构 * Munich University of Applied Sciences（慕尼黑应用科学大学） ； Technical University of Munich（慕尼黑技术大学） 纠错

专题命中 感知 ：occupancy(title,abstract)

"UWBCarGraz" Dataset for Car Occupancy Detection using Ultra-Wideband Radar

Jakob Möderl, Stefan Posch, Franz Pernkopf, Klaus Witrisal

专题命中 感知 ：occupancy(title,abstract)

Comments v1 (17.11.2023). 6 pages, 5 figures

SCOUT: A Lightweight Framework for Scenario Coverage Assessment in Autonomous Driving

Anil Yildiz, Sarah M. Thornton, Carl Hildebrandt, Sreeja Roy-Singh, Mykel J. Kochenderfer

机构 * Department of Aeronautics and Astronautics, Stanford University（航空航天工程系，斯坦福大学） ； Nuro Inc.（Nuro公司） 纠错

专题命中 感知 ：autonomous driving(title)；分类 cs.RO、cs.CV、cs.AI

AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems

Linfeng Liang, Chenkai Tan, Yao Deng, Yingfeng Cai, T. Y Chen, Xi Zheng

专题命中 感知 ：autonomous driving(title,abstract)

Optimized dynamic scheduling of an exclusive bus lane or high occupancy vehicle lane in a bimodal traffic corridor

Jiaqing Lu, Dian Sheng, Qianwen Guo, Paul Schonfeld

专题命中 感知 ：occupancy(title,abstract)

Comments This version is identical to the manuscript submitted to International Journal of Sustainable Transportation. It corrects inconsistencies present in the previous arXiv version and is the version under journal review. No scientific content has been altered

Testing Autonomous Driving Systems -- What Really Matters and What Doesn't

Changwen Li, Joseph Sifakis, Rongjie Yan, Jian Zhang

专题命中 感知 ：autonomous driving(title,abstract)

Self-driving technologies need the help of the public: A narrative review of the evidence

Jonathan Smith, Siddartha Khastgir

专题命中 感知 ：self-driving(title,abstract)

Minimal material, maximum coverage: Silicon Tracking System for high-occupancy conditions

M. Teklishyn, L. M. Collazo Sánchez, U. Frankenfeld, J. M. Heuser, O. Kshyvanskyi, J. Lehnert, D. A. Ramírez Zaldivar, D. Rodríguez Garcés, A. Rodríguez Rodríguez, C. J. Schmidt, P. Semeniuk, M. Shiroya, A. Sharma, A. Toia, O. Vasylyev

专题命中 感知 ：occupancy(title,abstract)

LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving

Sicen Guo, Ziwei Long, Zhiyuan Wu, Qijun Chen, Ioannis Pitas, Rui Fan

机构 * College of Electronics & Information Engineering, Tongji University（同济大学电子与信息工程学院） ； University of Thessaloniki（塞萨洛尼基大学） ； Shanghai Institute of Intelligent Science and Technology（上海智能科学技术研究院） ； Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院） ； Tongji University（同济大学） ； Xi’an Jiaotong University（西安交通大学） 纠错

专题命中 感知 ：autonomous driving(title)；分类 cs.RO、cs.CV、cs.AI

Comments 13 pages, 7 figures, 5 tables

Highway Managed Lane Usage and Tolling for Mixed Traffic Flows with Connected Automated Vehicles (CAVs) and High-Occupancy Vehicles (HOVs)

Max T. M. Ng, Hani S. Mahmassani

专题命中 感知 ：occupancy(title,abstract)

Comments 38 pages, 23 figures. This is the accepted version of a work that was published in Transportation Research Record

Journal ref Transportation Research Record 2678.4 (2024): 505-526

A space-time tracking algorithm for high occupancy events at future colliders

Massimo Casarsa, Sergo Jindariani, Luciano Ristori

专题命中 感知 ：occupancy(title,abstract)

Capacity Allocation and Pricing of High Occupancy Toll Lane Systems with Heterogeneous Travelers

Haripriya Pulyassary, Ruifan Yang, Zhanhao Zhang, Manxi Wu

专题命中 感知 ：occupancy(title,abstract)

On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving

Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe, M. Victoria Martínez, Unai Martínez-Corral, Óscar Mata Carballeira, Inés del Campo

专题命中 感知 ：autonomous driving(title)；分类 cs.CV、eess.IV、cs.AI

Journal ref 2023 Journal of Systems Architecture (JSA)

HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving

Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe, M. Victoria Martínez, Unai Martínez-Corral

专题命中 感知 ：autonomous driving(title)；分类 cs.CV、eess.IV、cs.AI

Journal ref 2023 IEEE Symposium Series on Computational Intelligence (SSCI)

Near-Range Environmental Perception for Inland Waterway Vessels: A Comparative Study of LiDAR and Automotive FMCW RADAR Sensors

R. Herrmann, S. Bose, I. Filip, D. Medina, R. Ziebold, S. Gehrig, T. Lenhard, M. Gardill

专题命中 感知 ：LiDAR(title,abstract)

Comments 9 pages, 11 figues, conference, included in conference proceedings of DGON POSNAV 2024

Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving

Kui Wang, Kazuma Nonomura, Zongdian Li, Tao Yu, Kei Sakaguchi, Omar Hashash, Walid Saad, Changyang She, Yonghui Li

专题命中 感知 ：autonomous driving(title,abstract)

Comments 14 pages, 9 figures