Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applications

Abstract

A critical requirement for automated driving systems is enabling situational awareness in dynamically changing environments. To that end vehicles will be equipped with diverse sensors, e.g., LIDAR, cameras, mmWave radar, etc. Unfortunately the sensing `coverage' is limited by environmental obstructions, e.g., other vehicles, buildings, people, objects etc. A possible solution is to adopt collaborative sensing amongst vehicles possibly assisted by infrastructure. This paper introduces new models and performance analysis for vehicular collaborative sensing and networking. In particular, coverage gains are quantified, as are their dependence on the penetration of vehicles participating in collaborative sensing. We also evaluate the associated communication loads in terms of the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) capacity requirements and how these depend on penetration. We further explore how collaboration with sensing capable infrastructure improves sensing performance, as well as the benefits in utilizing spatio-temporal dynamics, e.g., collaborating with vehicles moving in the opposite direction. Collaborative sensing is shown to greatly improve sensing performance, e.g., improves coverage from 20% to 80% with a 20% penetration. In scenarios with limited penetration and high coverage requirements, infrastructure can be used to both help sense the environment and relay data. Once penetration is high enough, sensing vehicles provide good coverage and data traffic can be effectively `offloaded' to V2V connectivity, making V2I resources available to support other in-car services.