自动驾驶

DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

Fuzhang Han, Han Zheng, Wenjun Huang, Rong Xiong, Yue Wang, Yanmei Jiao

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

Transformer-Based Sensor Fusion for Autonomous Driving: A Survey

Apoorv Singh

专题命中 多传感器融合 ：autonomous driving(title,abstract)；分类 cs.CV

Comments 5 pages, 1 figure

Brief Industry Paper: The Necessity of Adaptive Data Fusion in Infrastructure-Augmented Autonomous Driving System

Shaoshan Liu, Jianda Wang, Zhendong Wang, Bo Yu, Wei Hu, Yahui Liu, Jie Tang, Shuaiwen Leon Song, Cong Liu, Yang Hu

专题命中 多传感器融合 ：autonomous driving(title,abstract)；分类 cs.RO

Journal ref 28th IEEE Real-Time and Embedded Technology and Applications Symposium, 2022

2D LiDAR and Camera Fusion Using Motion Cues for Indoor Layout Estimation

Jieyu Li, Robert Stevenson

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.CV

Journal ref In 2021 IEEE 24th International Conference on Information Fusion (FUSION), pp. 1-6. IEEE, 2021

VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach

Thien-Minh Nguyen, Shenghai Yuan, Muqing Cao, Yang Lyu, Thien Hoang Nguyen, Lihua Xie

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking

Xingxing Zuo, Yulin Yang, Patrick Geneva, Jiajun Lv, Yong Liu, Guoquan Huang, Marc Pollefeys

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

Comments 8 pages; Accepted by a conference

Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding

Zhiyu Huang, Chen Lv, Yang Xing, Jingda Wu

专题命中 多传感器融合 ：autonomous driving(title,abstract)；分类 cs.RO

Multi-Modality Cascaded Fusion Technology for Autonomous Driving

Hongwu Kuang, Xiaodong Liu, Jingwei Zhang, Zicheng Fang

专题命中 多传感器融合 ：autonomous driving(title,abstract)；分类 cs.CV

Indoor Layout Estimation by 2D LiDAR and Camera Fusion

Jieyu Li, Robert L Stevenson

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.CV

Comments Fast track article for IS&T International Symposium on Electronic Imaging 2020: Computational Imaging XVIII

LIC-Fusion: LiDAR-Inertial-Camera Odometry

Xingxing Zuo, Patrick Geneva, Woosik Lee, Yong Liu, Guoquan Huang

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

Comments Accepted by conference

A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3D Reconstructions

Weikun Zhen, Yaoyu Hu, Jingfeng Liu, Sebastian Scherer

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

Comments Accepted by IEEE Robotics and Automation Letters and is to be presented at IROS2019, Macau

Real Time Lidar and Radar High-Level Fusion for Obstacle Detection and Tracking with evaluation on a ground truth

Hatem Hajri, Mohamed-Cherif Rahal

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System

Pan Wei, Lucas Cagle, Tasmia Reza, John Ball, James Gafford

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.CV

Comments 34 pages

Journal ref MDPI journal Electronics, 7(6), 84, May, 2018

Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

Chanoh Park, Peyman Moghadam, Soohwan Kim, Alberto Elfes, Clinton Fookes, Sridha Sridharan

专题命中 多传感器融合 ：LiDAR(title,abstract)；分类 cs.RO

Comments 8 pages, accepted to ICRA 2018 Demo video link: https://www.youtube.com/watch?v=QNNLncT9XmQ

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision

STAR-VLM：基于汽车雷达监督的时空视觉语言模型，用于运动与速度估计

Pou-Chun Kung, Aryaman Rao, Utkrisht Sahai, Hemanth Murali, Yi Liu, Rui-Yu Lin, Katherine A. Skinner

机构 * University of Michigan（密歇根大学） 纠错

专题命中 多传感器融合 ：LiDAR(abstract,abstract_cn)；autonomous driving(abstract)；分类 cs.RO、cs.CV

AI总结 该研究提出STAR-VLM框架，利用汽车雷达监督提升时空视觉语言模型的运动推理与度量速度估计能力，在驾驶场景相关任务上实现了优于特定任务方法的最先进性能。

SaLF: Sparse Local Fields for Multi-Sensor Rendering in Real-Time

SaLF：用于实时多传感器渲染的稀疏局部场

Yun Chen, Matthew Haines, Jingkang Wang, Sahil Jain, Krzysztof Baron-Lis, Sivabalan Manivasagam, Ze Yang, Raquel Urtasun

机构 * Waabi ； University of Toronto（多伦多大学） ； University of Waterloo（滑铁卢大学） 纠错

专题命中 多传感器融合 ：LiDAR(abstract,abstract_cn)；self-driving(abstract)；分类 cs.RO、cs.CV

AI总结 本文提出SaLF，一种高效的体素表示方法，支持多传感器仿真，具备快速训练和渲染能力，适用于多传感器自主系统评估。

Comments ICRA 2026. Project page: https://waabi.ai/salf/

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 多传感器融合 ：autonomous driving(abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.RO、cs.CV

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。

Availability-aware Sensor Fusion via Unified Canonical Space

Dong-Hee Paek, Seung-Hyun Kong

机构 * CCS Graduate School of Mobility（移动系统联合研究生院） ； KAIST（韩国科学技术院） 纠错

专题命中 多传感器融合 ：autonomous driving(abstract)；BEV(abstract)；LiDAR(abstract)；分类 cs.CV、cs.AI

Comments Accepted at NeurIPS 2025

Journal ref Proceedings of the Neural Information Processing Systems (NeurIPS 2025)

L2RDaS: Synthesizing 4D Radar Tensors for Model Generalization via Dataset Expansion

Woo-Jin Jung, Dong-Hee Paek, Seung-Hyun Kong

机构 * CCS Graduate School of Mobility, Korea Advanced Institute of Science and Technology（移动研究学院，韩国科学技术院） 纠错

专题命中 多传感器融合 ：autonomous driving(abstract)；BEV(abstract)；LiDAR(abstract)；分类 cs.CV、eess.IV

Comments 9 pages, 3 figures, Arxiv preprint

Velocity Driven Vision: Asynchronous Sensor Fusion Birds Eye View Models for Autonomous Vehicles

Seamie Hayes, Sushil Sharma, Ciarán Eising

专题命中 多传感器融合 ：autonomous driving(abstract)；BEV(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV

Comments This paper is a preprint of a paper submitted to the 26th Irish Machine Vision and Image Processing Conference (IMVIP 2024). If accepted, the copy of record will be available at IET Digital Library

Journal ref Proceedings of the Irish Machine Vision and Image Processing Conference 2024

HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework

Kai Lei, Zhan Chen, Shuman Jia, Xiaoteng Zhang

专题命中 多传感器融合 ：autonomous driving(abstract)；BEV(abstract)；LiDAR(abstract)；分类 cs.CV、cs.AI

Multimodal Framework for Explainable Autonomous Driving: Integrating Video, Sensor, and Textual Data for Enhanced Decision-Making and Transparency

Abolfazl Zarghani, Amirhossein Ebrahimi, Amir Malekesfandiari

专题命中 多传感器融合 ：autonomous driving(title,abstract)

See Further Than CFAR: a Data-Driven Radar Detector Trained by Lidar

Ignacio Roldan, Andras Palffy, Julian F. P. Kooij, Dariu M. Gavrila, Francesco Fioranelli, Alexander Yarovoy

专题命中 多传感器融合 ：LiDAR(title,abstract)

Comments Accepted for lecture presentation at IEEE RadarConf'24, Denver, USA

Journal ref 2024 IEEE Radar Conference (RadarConf24)

Autonomous Driving Vehicles Using Adaptive Learning Method for Data Fusion

Farhad Aghili

专题命中 多传感器融合 ：autonomous driving(title,abstract)

Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles

R. Spencer Hallyburton, Yupei Liu, Yulong Cao, Z. Morley Mao, Miroslav Pajic

专题命中 多传感器融合 ：LiDAR(title,abstract)

VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection

VLC Fusion：面向鲁棒目标检测的视觉-语言条件传感器融合

Aditya Taparia, Noel Ngu, Mario Leiva, Joshua Shay Kricheli, John Corcoran, Nathaniel D. Bastian, Gerardo Simari, Paulo Shakarian, Ransalu Senanayake

机构 * Arizona State University（亚利桑那州立大学） ； Department of Computer Science and Engineering, Universidad Nacional del Sur and Institute for Computer Science and Engineering（计算机科学与工程系，国家南方大学和计算机科学与工程研究所） ； U.S. Department of Defense（美国国防部） ； United States Military Academy（美国军事学院） ； Syracuse University（雪城大学） 纠错

专题命中 多传感器融合 ：LiDAR(abstract,abstract_cn)；autonomous driving(abstract)；分类 cs.CV

AI总结 本文提出VLC Fusion视觉-语言条件传感器融合框架，利用VLM捕捉环境线索动态调整模态权重，在多传感器融合目标检测任务中，于自动驾驶与军事目标数据集上较传统方法实现更优性能。

Comments 27 pages, 20 figures, Accepted for presentation at ECML PKDD 2026, Shortlisted for the Best Research Track Student Paper Award

EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

EPMF: 高效感知感知多传感器融合用于3D语义分割

Mingkui Tan, Zhuangwei Zhuang, Sitao Chen, Rong Li, Kui Jia, Qicheng Wang, Yuanqing Li

机构 * School of Software Engineering, South China University of Technology（南方科技大学软件工程学院） ； Pazhou Laboratory, Guangzhou, China（广州帕佐实验室） ； School of Electronic and Information Engineering, South China University of Technology（南方科技大学电子与信息工程学院） ； Minieye, Shenzhen, Guangdong, China（深圳Minieye公司） 纠错

专题命中 多传感器融合 ：LiDAR(abstract,abstract_cn)；autonomous driving(abstract)；分类 cs.CV

AI总结 提出一种高效感知多传感器融合方法EPMF，通过透视投影对齐点云与RGB图像，利用残差融合模块和感知损失提升3D语义分割性能，在nuScenes上mIoU超越RangeFormer 0.9%。

Comments 16 pages, 12 figures, 14 tables, IEEE TPAMI 2024, extended version of the ICCV2021 paper

TARS: Traffic-Aware Radar Scene Flow Estimation

TARS：面向交通的雷达场景流估计

Jialong Wu, Marco Braun, Dominic Spata, Matthias Rottmann

机构 * University of Wuppertal（乌尔姆大学） ； Osnabrück University（奥斯纳布吕克大学） ； Aptiv Services Deutschland GmbH（Aptiv Services德国公司） 纠错

专题命中 多传感器融合 ：LiDAR(abstract,abstract_cn)；autonomous driving(abstract)；分类 cs.CV

AI总结 本文提出TARS方法，通过交通层面的运动刚性提升雷达场景流估计，结合目标检测与场景流联合优化，构建交通向量场以实现交通层面的场景理解。

Journal ref Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 26075-26084

RF4D:Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes

RF4D:基于雷达的神经场用于户外动态场景的新型视角合成

Jiarui Zhang, Zhihao Li, Chong Wang, Bihan Wen

机构 * Nanyang Technological University, Singapore（南洋理工大学，新加坡） 纠错

专题命中 多传感器融合 ：autonomous driving(abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.CV

AI总结 RF4D通过引入雷达特定的神经场框架，提升户外动态场景中新型视角合成的鲁棒性和准确性。

CurriFlow: Curriculum-Guided Depth Fusion with Optical Flow-Based Temporal Alignment for 3D Semantic Scene Completion

Jinzhou Lin, Jie Zhou, Wenhao Xu, Rongtao Xu, Changwei Wang, Shunpeng Chen, Kexue Fu, Yihua Shao, Li Guo, Shibiao Xu

机构 * School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院） ； Ministry of Education（教育部） ； Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算机科学中心（济南国家超算中心）） ； Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）） ； Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing（山东省计算电力互联网与服务计算重点实验室） ； Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心） 纠错

专题命中 多传感器融合 ：autonomous driving(abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.CV