What question did this study set out to answer?

To review cooperative perception architectures in connected automated transit, assess collaboration across sensor fusion layers, and identify research directions for resilient real-world deployment.

October 1, 2025Proceedings of the IEEE

Cooperative Perception for Automated Driving: A Survey of Algorithms, Applications, and Future Directions

View Full Paper

Ask AI

Bookmark

Authors

CWChuheng WeiUniversity of California, Riverside GWGuoyuan WuUniversity of California, Riverside MBMatthew BarthUniversity of California, Riverside

Discussion

Member takes

Implication

Comprehensive review demonstrates multimodal sensor sharing enhances environmental awareness in automated vehicles, indicating key requirements for robust data fusion under adverse conditions.

Key Points

To review cooperative perception architectures in connected automated transit, assess collaboration across sensor fusion layers, and identify research directions for resilient real-world deployment.
Surveyed early-, intermediate-, and late-stage collaboration strategies for cooperative perception across multi-agent vehicle-to-everything (V2X) networks.
Evaluated multimodal sensor integration methodologies and cataloged limitations in current public datasets regarding sensor heterogeneity, adverse weather, and geographic scale.
Data sharing across vehicles and roadside units mitigates visual occlusion, expands effective sensing range, and improves object detection accuracy under restricted bandwidth and poor visibility.
Identified significant shortages in benchmark datasets representing diverse sensor setups, extreme environmental conditions, and large-scale spatial deployments.
Identified key future research frontiers, including adaptive fusion algorithms for disparate sensors, integration of high-definition maps, and end-to-end architectures utilizing large language models.

Cite This Study

Wei et al. (2025) studied this question.

synapsesocial.com/papers/6a8b0fab77493d51bfdecf60 https://doi.org/10.1109/jproc.2025.3608874

View Full Paper

Ask AI

Bookmark

October 1, 2025Proceedings of the IEEE

Cooperative Perception for Automated Driving: A Survey of Algorithms, Applications, and Future Directions

View Full Paper

Ask AI

Bookmark

Authors

CWChuheng WeiUniversity of California, Riverside GWGuoyuan WuUniversity of California, Riverside MBMatthew BarthUniversity of California, Riverside

Discussion

Member takes

Implication

Comprehensive review demonstrates multimodal sensor sharing enhances environmental awareness in automated vehicles, indicating key requirements for robust data fusion under adverse conditions.

Key Points

To review cooperative perception architectures in connected automated transit, assess collaboration across sensor fusion layers, and identify research directions for resilient real-world deployment.
Surveyed early-, intermediate-, and late-stage collaboration strategies for cooperative perception across multi-agent vehicle-to-everything (V2X) networks.
Evaluated multimodal sensor integration methodologies and cataloged limitations in current public datasets regarding sensor heterogeneity, adverse weather, and geographic scale.
Data sharing across vehicles and roadside units mitigates visual occlusion, expands effective sensing range, and improves object detection accuracy under restricted bandwidth and poor visibility.
Identified significant shortages in benchmark datasets representing diverse sensor setups, extreme environmental conditions, and large-scale spatial deployments.
Identified key future research frontiers, including adaptive fusion algorithms for disparate sensors, integration of high-definition maps, and end-to-end architectures utilizing large language models.

Cite This Study

Wei et al. (2025) studied this question.

synapsesocial.com/papers/6a8b0fab77493d51bfdecf60 https://doi.org/10.1109/jproc.2025.3608874

View Full Paper

Ask AI

Bookmark