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Autonomous driving has attracted significant attention from both academia andindustries, which is expected to offer a safer and more efficient drivingsystem. However, current autonomous driving systems are mostly based on asingle vehicle, which has significant limitations which still poses threats todriving safety. Collaborative perception with connected and autonomous vehicles(CAVs) shows a promising solution to overcoming these limitations. In thisarticle, we first identify the challenges of collaborative perception, such asdata sharing asynchrony, data volume, and pose errors. Then, we discuss thepossible solutions to address these challenges with various technologies, wherethe research opportunities are also elaborated. Furthermore, we propose ascheme to deal with communication efficiency and latency problems, which is achannel-aware collaborative perception framework to dynamically adjust thecommunication graph and minimize latency, thereby improving perceptionperformance while increasing communication efficiency. Finally, we conductexperiments to demonstrate the effectiveness of our proposed scheme.

Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities