Safe Driving Capacity of Autonomous Vehicles
This work addresses the challenge of standardizing safety assessments for autonomous driving systems, which is incremental as it builds on existing formal methods to propose new metrics.
The paper tackles the problem of defining and measuring safety and efficiency for autonomous vehicles by introducing formal definitions using linear temporal logic and proposing safe driving throughput and capacity metrics. It analyzes how these metrics are affected by different factors and shows that cooperative-based vehicles can achieve higher safe driving capacity than perception-based vehicles with proper design.
An excellent self-driving car is expected to take its passengers safely and efficiently from one place to another. However, different ways of defining safety and efficiency may significantly affect the conclusion we make. In this paper, we give formal definitions to the safe state of a road and safe state of a vehicle using the syntax of linear temporal logic (LTL). We then propose the concept of safe driving throughput (SDT) and safe driving capacity (SDC) which measure the amount of vehicles in the safe state on a road. We analyze how SDT is affected by different factors. We show the analytic difference of SDC between the road with perception-based vehicles (PBV) and the road with cooperative-based vehicles (CBV). We claim that through proper design, the SDC of the road filled with PBVs will be upper-bounded by the SDC of the road filled with CBVs.