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

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' and `reliability' of a single vehicle 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 infrastructures. This paper introduces new models and performance scaling analysis for vehicular collaborative sensing and networking. In particular, coverage and reliability gains are quantified, as are their dependence on the penetration of collaborative vehicles. We also evaluate the associated communication loads in terms of the V2V and/or V2I capacity requirements and how these depend on penetration. 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 enhanced reliability requirements, infrastructure can be used to 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.