多模态信息融合

Automatic Odometry-Less OpenDRIVE Generation From Sparse Point Clouds

Leon Eisemann, Johannes Maucher

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV、cs.RO

Comments 8 pages, 4 figures, 3 algorithms, 2 tables

Journal ref 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

Khaled El Madawy, Hazem Rashed, Ahmad El Sallab, Omar Nasr, Hanan Kamel, Senthil Yogamani

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；hybrid fusion(abstract)；分类 cs.CV、cs.RO

Comments Accepted for Oral Presentation at IEEE Intelligent Transportation Systems Conference (ITSC) 2019

EV-STLLM: Electric vehicle charging forecasting based on spatio-temporal large language models with multi-frequency and multi-scale information fusion

Hang Fan, Yunze Chai, Chenxi Liu, Weican Liu, Zuhan Zhang, Wencai Run, Dunnan Liu

专题命中 自动驾驶多传感器融合 ：information fusion(title)

Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning

Rabah Rahal, Abdelaziz Amara Korba, Yacine Ghamri-Doudane

机构 * Rabah Rahal Networks and Systems Laboratory (LRS), Badji Mokhtar Annaba University, Algeria（拉比·拉尔实验室，巴吉·莫克塔尔安纳巴大学，阿尔及利亚） ； Abdelaziz Amara korba Networks and Systems Laboratory (LRS), Badji Mokhtar Annaba University, Algeria（阿卜杜勒阿齐兹·阿马拉·科尔巴实验室，巴吉·莫克塔尔安纳巴大学，阿尔及利亚） ； L3I, University of La Rochelle, France（L3I，拉罗歇尔大学，法国） 纠错

专题命中 自动驾驶多传感器融合 ：multimodal fusion(title)

Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting

Ashutosh Sao, Nicolas Tempelmeier, Elena Demidova

专题命中 自动驾驶多传感器融合 ：information fusion(title)

Journal ref 24th IEEE International Conference on Intelligent Transportation Systems - ITSC2021

Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control

Jiqian Dong, Sikai Chen, Yujie Li, Runjia Du, Aaron Steinfeld, Samuel Labi

专题命中 自动驾驶多传感器融合 ：information fusion(title)

FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog

FogDrive：用于分级雾天感知的多模态合成驾驶数据集

Vansh Panwar

机构 * Indian Institute of Technology, Guwahati（印度理工学院古瓦哈提分校） 纠错

专题命中 自动驾驶多传感器融合 ：multi-modal fusion(abstract)；sensor fusion(abstract)；分类 cs.CV

AI总结 FogDrive是用于分级雾天感知的多模态合成驾驶数据集，基于CARLA模拟器构建，含多种传感器数据，模拟不同密度雾。通过跨两种范式建立基线基准，得出训练中混合多密度雾可收紧3D边界框几何形状等见解，将开源加速多模态研究。

LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving

LRC-WeatherNet：用于自动驾驶中实时天气类型分类的激光雷达、雷达和摄像头融合网络

Nour Alhuda Albashir, Lars Pernickel, Danial Hamoud, Idriss Gouigah, Eren Erdal Aksoy

机构 * Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research（哈姆斯塔德大学，信息科技学院，应用智能系统研究中心） 纠错

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

AI总结 本文提出LRC-WeatherNet，通过融合激光雷达、雷达和摄像头数据，实现在恶劣天气下的实时天气分类，优于单一传感器基线，且在MSU-4S数据集上表现优异。

Comments Accepted for publication at IEEE Intelligent Vehicles Symposium - IVS 2026

FIN: Fast Inference Network for Map Segmentation

Ruan Bispo, Tim Brophy, Reenu Mohandas, Anthony Scanlan, Ciarán Eising

机构 * Department of Electronic and Computer Engineering, University of Limerick（电子与计算机工程系，利默里克大学） ； Data Driven Computer Engineering Research Centre, University of Limerick（数据驱动计算机工程研究中心，利默里克大学） ； Lero, The Irish Software Research Centre, University of Limerick（Lero，爱尔兰软件研究中心，利默里克大学） 纠错

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

A Real-time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM

Zongbo Liao, Xuanxuan Zhang, Tianxiang Zhang, Zhi Li, Zhenqi Zheng, Zhichao Wen, You Li

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.RO

Comments 13 pages

From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model

Xiaojie Xu, Tianshuo Xu, Fulong Ma, Yingcong Chen

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

Comments Accepted at International Conference on Robotics and Automation(ICRA)

Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation

Wei Jong Yang, Guan Cheng Lee

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

Comments arXiv admin note: text overlap with arXiv:2309.11755

Group Equivariant BEV for 3D Object Detection

Hongwei Liu, Jian Yang, Jianfeng Zhang, Dongheng Shao, Jielong Guo, Shaobo Li, Xuan Tang, Xian Wei

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

Comments 8 pages,3 figures

MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review

Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, Zhiyong Feng

专题命中 自动驾驶多传感器融合 ：information fusion(abstract)；sensor fusion(abstract)；分类 cs.CV

VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion

Hanqi Zhu, Jiajun Deng, Yu Zhang, Jianmin Ji, Qiuyu Mao, Houqiang Li, Yanyong Zhang

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

Fast Obstacle Avoidance Motion in SmallQuadcopter operation in a Cluttered Environment

Chaitanyavishnu S. Gadde, Mohitvishnu S. Gadde, Nishant Mohanty, Suresh Sundaram

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.RO

Comments Submitted and Accepted to IEEE Conecct 2021

Multi-Modality Cascaded Fusion Technology for Autonomous Driving

Hongwu Kuang, Xiaodong Liu, Jingwei Zhang, Zicheng Fang

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；feature-level fusion(abstract)；分类 cs.CV

The ApolloScape Open Dataset for Autonomous Driving and its Application

Xinyu Huang, Peng Wang, Xinjing Cheng, Dingfu Zhou, Qichuan Geng, Ruigang Yang

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)；分类 cs.CV

Comments Version 4: Accepted by TPAMI. Version 3: 17 pages, 10 tables, 11 figures, added the application (DeLS-3D) based on the ApolloScape Dataset. Version 2: 7 pages, 6 figures, added comparison with BDD100K dataset

RG-Attn: Radian Glue Attention for Multi-modality Multi-agent Cooperative Perception

Lantao Li, Kang Yang, Wenqi Zhang, Xiaoxue Wang, Chen Sun

机构 * Sony (China) Limited（索尼（中国）有限公司） ； Renmin University of China（中国人民大学） 纠错

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、eess.IV、cs.RO

Comments Accepted by ICCV 2025 DriveX workshop (Final Version)

Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera

Lei Cheng, Siyang Cao

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；分类 eess.IV、eess.SP、cs.RO

Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving

Florian Sauerbeck, Dominik Kulmer, Markus Pielmeier, Maximilian Leitenstern, Christoph Weiß, Johannes Betz

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、eess.SP、cs.RO

Comments Accepted and presented at IEEE Sensors Conference 2023

Journal ref IEEE Sensors Conference 2023

Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection

Junjie Shen, Yunpeng Luo, Ziwen Wan, Qi Alfred Chen

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)

A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling

Xudong Pan, Qifan Xiao, Mi Zhang, Min Yang

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；multi-sensor fusion(abstract)

Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors

Dominik Notz, Felix Becker, Thomas Kühbeck, Daniel Watzenig

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、eess.IV、cs.RO

Comments 8 pages, 9 figures

Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups

几何感知鱼眼-激光雷达融合用于低重叠设置下的鲁棒3D目标检测

Xiangzhong Liu, Xihao Wang, Hao Shen

机构 * Technical University of Munich（慕尼黑工业大学） 纠错

专题命中 自动驾驶多传感器融合 ：hybrid fusion(abstract)；分类 cs.CV、cs.RO

AI总结 针对稀疏视角下鱼眼相机与激光雷达的几何畸变和低重叠问题，提出几何感知混合融合框架，通过畸变感知LSS模块和双注意力校正模块实现极坐标与笛卡尔特征融合，在三个基准上提升检测精度。

Comments 8 pages, 4 figures, submitted to RA-L

MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning

MTA-RL：通过多模态Transformer-based 3D affordances和强化学习实现鲁棒的城市驾驶

Guangli Chen, Dianzhao Li, Wenjian Zhong, Bangquan Xie, Ostap Okhrin

机构 * Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University（东莞智能装备与智能制造重点实验室，先进工程学院，大湾大学） ； Chair of Applied Statistics, Technische Universität Dresden（应用统计学教授职位，德累斯顿技术大学） ； Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据解析与人工智能中心（ScaDS.AI）） ； College of Automation, Guangdong University of Technology（自动化学院，广东技术大学） 纠错

专题命中 自动驾驶多传感器融合 ：multi-modal fusion(abstract)；分类 cs.CV、cs.RO

AI总结 本文提出MTA-RL框架，通过多模态Transformer-based 3D affordances和强化学习实现可靠的城市自动驾驶，提升样本效率和稳定性，优于现有基线方法。

nuScenes Revisited: Progress and Challenges in Autonomous Driving

nuScenes 重新审视：自动驾驶领域的进展与挑战

Whye Kit Fong, Venice Erin Liong, Kok Seang Tan, Holger Caesar

专题命中 自动驾驶多传感器融合 ：sensor fusion(abstract)；分类 cs.CV、cs.RO

AI总结 本文重新审视nuScenes数据集，探讨其在自动驾驶发展中的核心作用，分析其技术细节及对后续研究的影响，总结多模态传感器融合与标准化评估的关键贡献。

Comments 18 pages, 17 figures

FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection

FOMO-3D：利用视觉基础模型进行长尾3D目标检测

Anqi Joyce Yang, James Tu, Nikita Dvornik, Enxu Li, Raquel Urtasun

机构 * Waabi ； University of Toronto（多伦多大学） 纠错

专题命中 自动驾驶多传感器融合 ：multi-modal fusion(abstract)；分类 cs.CV、cs.RO

AI总结 FOMO-3D通过利用视觉基础模型的丰富先验知识和多模态融合设计，提升长尾3D目标检测的性能。

Comments Published at 9th Annual Conference on Robot Learning (CoRL 2025)

OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport

OT-Drive: 基于最优传输的离群分布越野可通行区域分割

Zhihua Zhao, Guoqiang Li, Chen Min, Kangping Lu

机构 * School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院） ； Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所） ； Shandong Pengxiang Automobile Co., Ltd（山东鹏翔汽车有限公司） 纠错

专题命中 自动驾驶多传感器融合 ：multi-modal fusion(abstract)；分类 cs.CV、cs.RO

AI总结 OT-Drive通过最优传输方法实现离群分布越野可通行区域分割，提升自动驾驶在复杂环境下的鲁棒性和泛化能力。

Comments 9 pages, 8 figures, 6 tables. This work has been submitted to the IEEE for possible publication. Code will be released upon acceptance

MMDrive: Interactive Scene Understanding Beyond Vision with Multi-representational Fusion

MMDrive: 通过多表示融合超越视觉的交互场景理解

Minghui Hou, Wei-Hsing Huang, Shaofeng Liang, Daizong Liu, Tai-Hao Wen, Gang Wang, Runwei Guan, Weiping Ding

机构 * organization= College of Computer Science ； Technology, Jilin University , city= Changchun , country= China ； organization= Georgia Institute of Technology , city= Atlanta , country= USA ； organization= Qingdao Institute of Software, College of Computer Science ； Technology, China University of Petroleum (East China) , city= Qingdao , country= China ； organization= Institute for Math \& AI, Wuhan University , city= Wuhan , country= China ； organization= University of Michigan, Ann Arbor , country= USA ； organization= Thrust of Artificial Intelligence, Hong Kong University of Science ； organization= School of Artificial Intelligence ； Computer Science, Nantong University , city= Nantong , country= China 纠错

专题命中 自动驾驶多传感器融合 ：information fusion(abstract)；分类 cs.CV、cs.RO

AI总结 MMDrive通过融合占用图、LiDAR点云和文本描述，实现超越视觉的三维场景理解，提升自动驾驶的多模态推理能力。