Time-Sensitive Cooperative Perception for Real-Time Data Sharing over Vehicular Communications: Overview, Challenges, and Future Directions

Cooperative perception is a prospective application to improve road safety by having connected autonomous vehicles (CAVs) exchange their raw or processed sensor data over vehicular communications. Since CAVs heavily rely on sensor-based perception, including vision cameras, LiDARs, and radars, cooperative perception has an immense potential to improve road safety. At the same time, a variety of sensors and edge servers have been widely deployed in smart cities, and such sensors and servers might be able to empower CAVs on public roads. In this article, we comprehensively study such cooperative perception for overview, technical challenges, practical requirements, prospective system designs, current approaches, and future research directions. In particular, we focus on the time sensitivity of the cooperative perception frameworks, in which delays of computing and communications lead to detection errors. In addition, we comprehensively study sensor fusion frameworks and models to improve the detection accuracy and reliability of CAVs while avoiding information flooding and/or rumor spreading. Since network congestion might lead to packet collisions and/or delays, congestion control might be essential to use cooperative perception in practice. Finally, we discuss the technical and ethical challenges of using cooperative perception on public roads and conclude with future research directions.