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Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving. However,…

Computer Vision and Pattern Recognition · Computer Science 2022-07-19 Yiming Li , Dekun Ma , Ziyan An , Zixun Wang , Yiqi Zhong , Siheng Chen , Chen Feng

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a…

Computer Vision and Pattern Recognition · Computer Science 2022-08-09 Runsheng Xu , Hao Xiang , Zhengzhong Tu , Xin Xia , Ming-Hsuan Yang , Jiaqi Ma

With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single-agent…

Artificial Intelligence · Computer Science 2025-09-30 An Guo , Shuoxiao Zhang , Enyi Tang , Xinyu Gao , Haomin Pang , Haoxiang Tian , Yanzhou Mu , Wu Wen , Chunrong Fang , Zhenyu Chen

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything cooperative perception, which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Tao Huang , Jianan Liu , Xi Zhou , Dinh C. Nguyen , Mostafa Rahimi Azghadi , Yuxuan Xia , Qing-Long Han , Sumei Sun

Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the field of view. However, past research has predominantly…

Computer Vision and Pattern Recognition · Computer Science 2025-09-05 Seth Z. Zhao , Huizhi Zhang , Zhaowei Li , Juntong Peng , Anthony Chui , Zewei Zhou , Zonglin Meng , Hao Xiang , Zhiyu Huang , Fujia Wang , Ran Tian , Chenfeng Xu , Bolei Zhou , Jiaqi Ma

Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object detection to avoid collision while its field of view is…

Robotics · Computer Science 2023-09-20 Minh-Quan Dao , Julie Stephany Berrio , Vincent Frémont , Mao Shan , Elwan Héry , Stewart Worrall

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the…

Computer Vision and Pattern Recognition · Computer Science 2025-06-23 Naibang Wang , Deyong Shang , Yan Gong , Xiaoxi Hu , Ziying Song , Lei Yang , Yuhan Huang , Xiaoyu Wang , Jianli Lu

Perception for automated driving is largely based on onboard environmental sensors, such as cameras and radar, which are cost-effective but limited by line-of-sight and field-of-view constraints. These inherent limitations may cause onboard…

Computer Vision and Pattern Recognition · Computer Science 2026-05-04 Lukas Ostendorf , Lennart Reiher , Onn Haran , Lutz Eckstein

With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of…

Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets…

Computer Vision and Pattern Recognition · Computer Science 2024-12-18 Hao Xiang , Zhaoliang Zheng , Xin Xia , Runsheng Xu , Letian Gao , Zewei Zhou , Xu Han , Xinkai Ji , Mingxi Li , Zonglin Meng , Li Jin , Mingyue Lei , Zhaoyang Ma , Zihang He , Haoxuan Ma , Yunshuang Yuan , Yingqian Zhao , Jiaqi Ma

The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety…

Cooperative perception through vehicle-to-everything (V2X) has garnered significant attention in recent years due to its potential to overcome occlusions and enhance long-distance perception. Great achievements have been made in both…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Rongsong Li , Xin Pei

Cooperative perception has been widely used in autonomous driving to alleviate the inherent limitation of single automated vehicle perception. To enable cooperation, vehicle-to-vehicle (V2V) communication plays an indispensable role. This…

Signal Processing · Electrical Eng. & Systems 2023-11-20 Chenguang Liu , Yunfei Chen , Jianjun Chen , Ryan Payton , Michael Riley , Shuang-Hua Yang

Vehicle-to-Everything (V2X) cooperation is reshaping traffic safety from an ego-centric sensing problem into one of collective intelligence. This survey structures recent progress within a unified Sensor-Perception-Decision (SPD) framework…

Systems and Control · Electrical Eng. & Systems 2025-12-02 Jiaxun Zhang , Qian Xu , Zhenning Li , Chengzhong Xu , Keqiang Li

Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off…

Computer Vision and Pattern Recognition · Computer Science 2022-09-27 Yue Hu , Shaoheng Fang , Zixing Lei , Yiqi Zhong , Siheng Chen

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the…

Computer Vision and Pattern Recognition · Computer Science 2023-09-01 Si Liu , Chen Gao , Yuan Chen , Xingyu Peng , Xianghao Kong , Kun Wang , Runsheng Xu , Wentao Jiang , Hao Xiang , Jiaqi Ma , Miao Wang

Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging information with neighbor agents through V2X communication.…

Robotics · Computer Science 2024-02-29 Shunli Ren , Zixing Lei , Zi Wang , Mehrdad Dianati , Yafei Wang , Siheng Chen , Wenjun Zhang

Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission. However, as more agents participate, existing frameworks face two key challenges: (1) the participating agents are…

Computer Vision and Pattern Recognition · Computer Science 2025-11-14 Yueran Zhao , Zhang Zhang , Chao Sun , Tianze Wang , Chao Yue , Nuoran Li

This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X). It highlights the latest developments in…

Computer Vision and Pattern Recognition · Computer Science 2024-04-23 Melih Yazgan , Mythra Varun Akkanapragada , J. Marius Zoellner

Cooperative perception enabled by Vehicle-to-Everything (V2X) communication holds significant promise for enhancing the perception capabilities of autonomous vehicles, allowing them to overcome occlusions and extend their field of view.…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Hao Xiang , Zhaoliang Zheng , Xin Xia , Seth Z. Zhao , Letian Gao , Zewei Zhou , Tianhui Cai , Yun Zhang , Jiaqi Ma
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