Safety-aware connected and automated vehicle information sharing for efficient collaborative perception

Accurate perception is essential for the safe operation of connected and automated vehicles. However, a single vehicle is limited to its own viewpoint, making it vulnerable to occlusion, restricted sensing range, and detection failures in complex traffic scenes. Collaborative perception—where vehicles share sensing information—can address these shortcomings. Nevertheless, existing approaches often transmit full feature maps or dense sensor data, imposing significant communication overhead that hinders practical deployment.

 

To address this limitation, researchers from the Department of Automation at Tsinghua University and the Institute of Intelligent Transportation Systems at Zhejiang University developed Which2comm, an efficient collaborative perception framework for connected and automated vehicles. Instead of sharing broad region-level feature maps, the framework transmits compact object-level semantic information, termed Semantic Detection Boxes (SemDBs). These sparse features retain key spatial and semantic details necessary for 3D object detection while greatly reducing the data volume that must be exchanged between vehicles.

 

A central innovation of the framework is its safety-risk-aware collaboration strategy. Rather than treating all vehicles equally in communication, Which2comm adaptively selects the most informative collaborative agents based on their relevance to driving safety. The framework also incorporates temporal feature fusion and relative time encoding, enabling better handling of communication latency and asynchronous data from different vehicles.

 

The researchers evaluated Which2comm on multiple collaborative perception datasets, including V2XSet, the real-world V2V4Real dataset, and a newly constructed large-scale dataset named Multi-V2X. Multi-V2X comprises 146,000 LiDAR frames, 549,000 RGB images, and approximately 4.219 million 3D annotation boxes, supporting high-density connected-vehicle scenarios with up to 31 connected agents and a connected-vehicle penetration rate of 86.21%.

 

Experimental results demonstrate that Which2comm achieves strong detection performance at a very low communication cost. On V2XSet, it attains 0.929 AP@0.5 and 0.833 AP@0.7 while requiring only 0.022 MB of transmitted data. The framework also outperforms existing collaborative perception methods on Multi-V2X and V2V4Real, showing robustness under communication latency and scalability in dense connected-vehicle environments.

 

The study indicates that object-level sparse communication can render collaborative perception more efficient, scalable, and practical for future intelligent transportation systems. By reducing communication redundancy while maintaining high perceptual accuracy, Which2comm offers a promising step toward real-time cooperative perception in connected and automated transportation systems.

 

Looking forward, the researchers plan to extend the framework to more complex real-world scenarios, such as multi-modal sensor fusion, localization uncertainty, and packet loss, further enhancing the model’s applicability and readiness.

 

The team published their study in Communications in Transportation Research (https://doi.org/10.26599/COMMTR.2026.9640048).

 

DOI Link:

https://doi.org/10.26599/COMMTR.2026.9640048

 

About Communications in Transportation Research

Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University, and Professor Xiaopeng (Shaw) Li from University of Wisconsin–Madison. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.

 

It has been indexed in SCIE, SSCI, Ei Compendex, Scopus, CSTPCD, CSCD, OAJ, DOAJ, TRID and other databases. It was selected as Q1 Top Journal in the Engineering and Technology category of the Chinese Academy of Sciences (CAS) Journal Ranking List. In 2022, it was selected as a High-Starting-Point new journal project of the “China Science and Technology Journal Excellence Action Plan”. In 2024, it was selected as the Support the Development Project of “High-Level International Scientific and Technological Journals”. The same year, it was also chosen as an English Journal Tier Project of the “China Science and Technology Journal Excellence Action Plan PhaseⅡ”. In 2024, it received the first impact factor (2023 IF) of 12.5, ranking Top1 (1/58, Q1) among all journals in "TRANSPORTATION" category. In 2026, its 2025 IF was announced as 12.7, maintaining the Top1 position (1/66, Q1) in the same category.

 

From Volume 6 (2026), Communications in Transportation Research will be published by Tsinghua University Press on the SciOpen platform with the official journal website at https://www.sciopen.com/journal/2097-5023. We kindly request that all new manuscript submissions be made through the journal’s submission system at https://mc03.manuscriptcentral.com/commtr. For any submission-related inquiries, please contact the Editorial Office at commtr_e@mail.tsinghua.edu.cn.

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