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Title: Edge-Assisted Collaborative Perception in Autonomous Driving: A Reflection on Communication Design
Collaborative perception enables autonomous driving vehicles to share sensing or perception data via broadcast-based vehicle-to-everything (V2X) communication technologies such as Cellular-V2X (C-V2X), hoping to enable accurate perception in face of inaccurate perception results by each individual vehicle. Nevertheless, the V2X communication channel remains a significant bottleneck to the performance and usefulness of collaborative perception due to limited bandwidth and ad hoc communication scheduling. In this paper, we explore challenges and design choices for V2X-based collaborative perception, and propose an architecture that lever-ages the power of edge computing such as road-side units for central communication scheduling. Using NS-3 simulations, we show the performance gap between distributed and centralized C-V2X scheduling in terms of achievable throughput and communication efficiency, and explore scenarios where edge assistance is beneficial or even necessary for collaborative perception.
Authors:
; ;
Award ID(s):
2045539 2007391
Publication Date:
NSF-PAR ID:
10328839
Journal Name:
ACM/IEEE Symposium on Edge Computing (SEC) Workshops
Sponsoring Org:
National Science Foundation
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