STC: Spatial and Temporal Clustering for Cooperative Perception System

Transportation safety is a very important concern for many decision-makers. Not only does it aim at preventing vehicle accidents, but also saves the lives of individuals who may be distracted while crossing the streets. Various traffic conditions can hinder the visibility of pedestrians and other vehicles when only relying on the local sensors attached to these AVs. Therefore, Cooperative Perception (CP) among Connected Autonomous Vehicles (CAV) is suggested to overcome this problem by utilizing inter-vehicle communication. However, communication network limitations, including limited bandwidth, packet loss, operator compatibilities, or even lack of coverage, can significantly impede the performance of these cooperative perception solutions. To this end, we propose a spatial safety-aware clustering algorithm. This algorithm clusters the perceived objects across AVs. This effective idea allows a decrease in communication payload by approximately 20% and efficiently increases the information reception over an infrastructure-less Vehicle-to-Vehicle (V2V) network by 10% compared to ETSI. Furthermore, we suggest integration of the spatial clustering algorithm with existing baselines, showing improvements of up to 18% for road object perception. Lastly, we propose a Spatial and Temporal Clustering (STC) approach that performs a clustering for information sent over the time domain in addition to the spatial clustering. This further decreases the payload by 41% and increases the perception up to 37% while showing more than a 12X increase in the reception of safety-relevant information compared to ETSI.