Cooperative Perception for Connected Autonomous Vehicles Under Constrained V2V Networking

Cooperative Perception (CP) is a newly emerged technique that can be used to enhance the safety of Autonomous Vehicles (AVs) when relying merely on the AV's own camera may not yield accurate results. Examples of such conditions are delayed or low object detection accuracy under foggy weather, winding roads, and blocked camera vision by the front vehicle. A few CP methods have been recently proposed to enhance the quality of AV perception through cooperative perception. Despite their success, these methods have a few limitations, such as adopting unrealistic assumptions about perfect communication and unconstrained resources as well as having access to large training datasets. In this paper, we use the LTE Release 14 Mode 4 side-link communication to implement CP by selective V2V communication for situational awareness. Our method is based on a demand-response mechanism and solving an optimization problem to employ helper vehicles, considering networking performance metrics (such as packet drop rate), available resources (radio blocks in LTE-V), extended visual range (the total road segment covered), and more importantly the ultimate perception quality of the selected vehicles. Our preliminary results demonstrate a significant gain for the proposed method compared to a conventional single-camera vision.