自动驾驶

CaRLi-V: Camera-RADAR-LiDAR Point-Wise 3D Velocity Estimation

CaRLi-V：摄像头-雷达-激光雷达点云三维速度估计

Landson Guo, Andres M. Diaz Aguilar, William Talbot, Turcan Tuna, Marco Hutter, Cesar Cadena

机构 * ETH Zürich（苏黎世联邦理工学院） ； Robotic Systems Lab (RSL), ETH Zürich（苏黎世联邦理工学院机器人系统实验室） 纠错

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

AI总结 本文提出CaRLi-V方法，结合雷达、激光雷达和摄像头数据，通过雷达速度立方体、光流和激光雷达测距，实现点云三维速度估计，优于现有场景流方法。

CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning

CLRNet：基于深度学习的无目标外校准方法用于摄像头、激光雷达和4D雷达

Marcell Kegl, Andras Palffy, Csaba Benedek, Dariu M. Gavrila

机构 * Hungarian Research Network Institute for Computer Science and Control (HUN-REN SZTAKI)（匈牙利研究网络计算机科学与控制研究所） ； Pázmány Péter Catholic University（帕兹曼·佩斯特天主教大学） ； Intelligent Vehicles Group, TU Delft（智能车辆组，代尔夫特理工大学） ； Perciv AI 纠错

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

AI总结 本文提出CLRNet，一种多模态端到端深度学习网络，用于摄像头、激光雷达和4D雷达的联合校准或配对校准，通过等距投影、基于摄像头的深度图像预测和雷达通道等方法，提升校准精度。

Comments Submitted to IEEE Transactions on Intelligent Vehicles

From Features to Reference Points: Lightweight and Adaptive Fusion for Cooperative Autonomous Driving

从特征到参考点：轻量且自适应的融合用于协同自动驾驶

Yongqi Zhu, Morui Zhu, Qi Chen, Deyuan Qu, Isabella Luo, Song Fu, Qing Yang

机构 * University of North Texas（北卡罗来纳大学达灵顿分校） ； Toyota InfoTech Labs（丰田信息技术实验室） ； The Hockaday School（霍克达伊学校） 纠错

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

AI总结 RefPtsFusion通过交换紧凑的参考点实现轻量且自适应的协同自动驾驶融合，显著降低通信开销并保持感知性能。

Comments v2: Updated author list to reflect contributions to this work

VP-AutoTest: A Virtual-Physical Fusion Autonomous Driving Testing Platform

VP-AutoTest：一种虚拟-物理融合的自动驾驶测试平台

Yiming Cui, Shiyu Fang, Jiarui Zhang, Yan Huang, Chengkai Xu, Bing Zhu, Hao Zhang, Peng Hang, Jian Sun

机构 * Department of Traffic Engineering & Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（交通工程系及道路交通工程重点实验室，教育部，同济大学） ； College of Electronic and Information Engineering, Department of Control Science and Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University（电子信息学院、控制科学与工程系及上海智能科学技术研究院，同济大学） ； State Key Laboratory of Automotive Simulation and Control, Jilin University（汽车仿真与控制国家重点实验室，吉林大学） 纠错

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

AI总结 VP-AutoTest通过整合多种虚拟和物理元素，实现自动驾驶测试的高仿真与高效能，解决传统测试方法的局限性。

Target Tracking via LiDAR-RADAR Sensor Fusion for Autonomous Racing

Marcello Cellina, Matteo Corno, Sergio Matteo Savaresi

机构 * Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano（电子、信息与生物工程系（DEIB），米兰理工大学） 纠错

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

Comments IEEE Conference, 6 pages

REOcc: Camera-Radar Fusion with Radar Feature Enrichment for 3D Occupancy Prediction

Chaehee Song, Sanmin Kim, Hyeonjun Jeong, Juyeb Shin, Joonhee Lim, Dongsuk Kum

机构 * School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院电子工程学院） ； Department of Automobile and IT Convergence, Kookmin University（韩国庆熙大学汽车与IT融合系） ； Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院移动技术研究生院） ； Robotics Program, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院机器人项目） 纠错

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

Comments IROS 2025

Modeling and Scheduling of Fusion Patterns in Autonomous Driving Systems (Extended Version)

Hoora Sobhani, Hyoseung Kim

机构 * University of California, Riverside (UCR)（加州大学河滨分校） 纠错

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

Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments

Haoxuan Jiang, Peicong Qian, Yusen Xie, Linwei Zheng, Xiaocong Li, Ming Liu, Jun Ma

机构 * The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）） ； Shenzhen Unity Drive Innovation Technology Co., Ltd.（深圳Unity Drive创新科技有限公司） ； College of Information Science and Technology, Eastern Institute of Technology（信息科学与技术学院，东方技术学院） 纠错

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

AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments

Chenglong Qian, Yang Xu, Xiufang Shi, Jiming Chen, Liang Li

机构 * College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院） ； College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院） 纠错

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

Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion

Ni Ou, Zhuo Chen, Xinru Zhang, Junzheng Wang

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

Comments This article is an earlier version of the article arXiv:2506.14706, and most of its content is duplicated.

Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving

Tuong Do, Binh X. Nguyen, Quang D. Tran, Erman Tjiputra, Te-Chuan Chiu, Anh Nguyen

机构 * AIOZ ； Department of Computer Science, NTHU（计算机科学系） ； Department of Computer Science, University of Liverpool（计算机科学系） 纠错

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

Comments Accepted in IROS 2025

RoadFormer : Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving

Tianze Wang, Zhang Zhang, Chao Sun

机构 * Beijing Institute of Technology（北京理工大学） 纠错

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

Thermal-LiDAR Fusion for Robust Tunnel Localization in GNSS-Denied and Low-Visibility Conditions

Lukas Schichler, Karin Festl, Selim Solmaz, Daniel Watzenig

机构 * Virtual Vehicle Research GmbH（虚拟车辆研究有限公司） 纠错

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

Comments Submitted to IAVVC 2025

FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback

Kangan Qian, Ziang Luo, Sicong Jiang, Zilin Huang, Jinyu Miao, Zhikun Ma, Tianze Zhu, Jiayin Li, Yangfan He, Zheng Fu, Yining Shi, Boyue Wang, Hezhe Lin, Ziyu Chen, Jiangbo Yu, Xinyu Jiao, Mengmeng Yang, Kun Jiang, Diange Yang

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

Comments 8 pages, 4 figures

Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping

Max Peter Ronecker, Xavier Diaz, Michael Karner, Daniel Watzenig

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

Comments Accepted at 35th IEEE Intelligent Vehicles Symposium (IV) 2024

LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering

Sheng Hong, Junjie He, Xinhu Zheng, Chunran Zheng, Shaojie Shen

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

Adaptive LiDAR-Radar Fusion for Outdoor Odometry Across Dense Smoke Conditions

Chiyun Noh, Ayoung Kim

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

Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion

Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Limin Wang

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

Comments Accepted to TPAMI 2023

Imaging radar and LiDAR image translation for 3-DOF extrinsic calibration

Sangwoo Jung, Hyesu Jang, Minwoo Jung, Ayoung Kim, Myung-Hwan Jeon

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

CRPlace: Camera-Radar Fusion with BEV Representation for Place Recognition

Shaowei Fu, Yifan Duan, Yao Li, Chengzhen Meng, Yingjie Wang, Jianmin Ji, Yanyong Zhang

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

Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

JunDa Cheng, Wei Yin, Kaixuan Wang, Xiaozhi Chen, Shijie Wang, Xin Yang

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

Comments Accepted to CVPR 2024

Degradation Resilient LiDAR-Radar-Inertial Odometry

Morten Nissov, Nikhil Khedekar, Kostas Alexis

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

Comments 8 pages, 5 figures. Accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2024

LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning

Pedram Agand, Mohammad Mahdavian, Manolis Savva, Mo Chen

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

Comments 11 pages, 2 figures, 3 tables. CVPR Workshops (VCAD). 2023

An Efficient Probabilistic Solution to Mapping Errors in LiDAR-Camera Fusion for Autonomous Vehicles

Dan Shen, Zhengming Zhang, Renran Tian, Yaobin Chen, Rini Sherony

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

ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking

Tara Sadjadpour, Rares Ambrus, Jeannette Bohg

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

Comments 8 pages, 1 figure

INF: Implicit Neural Fusion for LiDAR and Camera

Shuyi Zhou, Shuxiang Xie, Ryoichi Ishikawa, Ken Sakurada, Masaki Onishi, Takeshi Oishi

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

Comments Accepted to IROS 2023. (project page: https://ShuyiZhou495.github.io/inf-project-page/)

Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

Kaavya Rekanar, Ciarán Eising, Ganesh Sistu, Martin Hayes

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

Journal ref Proceedings of the Irish Machine Vision and Image Processing Conference 2023

Probabilistic Multimodal Depth Estimation Based on Camera-LiDAR Sensor Fusion

Johan S. Obando-Ceron, Victor Romero-Cano, Sildomar Monteiro

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

Comments 15 pages. arXiv admin note: text overlap with arXiv:1411.6387, arXiv:1702.02706 by other authors

Journal ref Machine Vision and Applications journal, 2023

Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?

Keenan Burnett, Yuchen Wu, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot

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

Comments Version 3: Accepted to RA-L, presented at IROS 2022. Localization results updated due to improved ground truth and calibration. Also switched Huber Loss for Cauchy Loss for the radar-based approaches

AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars

Carlos Crispim-Junior, Romain Guesdon, Christophe Jallais, Florent Laroche, Stephanie Souche-Le Corvec, Laure Tougne Rodet

专题命中 多传感器融合 ：self-driving(title,abstract)；分类 cs.AI

Comments This paper is currently under review by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)