While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the @math evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims. We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning perception and control algorithms. Using adaptive importance-sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior. We demonstrate our framework on a highway scenario, accelerating system evaluation by @math - @math times over naive Monte Carlo sampling methods and @math - @math times (where @math is the number of processors) over real-world testing.
To summarize, a fundamental tradeoff emerges when comparing the requirements of our risk-based framework to other testing paradigms, such as real-world testing or formal verification. Real-world testing endangers the public but is still in some sense a gold standard. Verified subsystems provide evidence that the AV should drive safely even if the estimated distribution shifts, but verification techniques are limited by computational intractability as well as the need for both white-box models and the completeness of specifications that assign blame ( @cite_44 ). In turn, our risk-based framework is most useful when the base distribution @math is accurate, but even when @math is misspecified, our adaptive importance sampling techniques can still efficiently identify dangerous scenarios, especially those that may be missed by verification methods assigning blame. Our framework offers significant speedups over real-world testing and allows efficient evaluation of black-box AV input output behavior, providing a powerful tool to aid in the design of safe AVs.
@cite_44
2749747771
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.