On a Formal Model of Safe and Scalable Self-driving Cars
This addresses safety and cost challenges for the autonomous driving industry, offering a foundational approach rather than an incremental improvement.
The paper tackles the lack of standardized safety assurance and scalability in self-driving cars by proposing a formal model called Responsibility-Sensitive Safety (RSS) and a scalable system design to prevent industry stagnation.
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- what are the minimal requirements that every self-driving car must satisfy, and how can we verify these requirements. The second parameter is scalability --- engineering solutions that lead to unleashed costs will not scale to millions of cars, which will push interest in this field into a niche academic corner, and drive the entire field into a "winter of autonomous driving". In the first part of the paper we propose a white-box, interpretable, mathematical model for safety assurance, which we call Responsibility-Sensitive Safety (RSS). In the second part we describe a design of a system that adheres to our safety assurance requirements and is scalable to millions of cars.