Machine Learning

On a Formal Model of Safe and Scalable Self-driving Cars

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  • arXiv
    5 pts

    On a Formal Model of Safe and Scalable Self-driving Cars

    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. 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.

    On a Formal Model of Safe and Scalable Self-driving Cars
    by Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
    https://arxiv.org/pdf/1708.06374v2.pdf

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