Self-Driving Car Safety Quantification via Component-Level Analysis
| Autor: | Steve Dale Keen, Tilo Wiklund, Auste Grigaite, Antanas Kalkauskas, Juozas Vaicenavicius, Ignas Vysniauskas |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science Probability (math.PR) General Medicine Statistics - Applications Automotive engineering Computer Science Applications Artificial Intelligence (cs.AI) Self driving Artificial Intelligence Control and Systems Engineering Component (UML) Automotive Engineering FOS: Mathematics Applications (stat.AP) Mathematics - Probability |
| Zdroj: | SAE International Journal of Connected and Automated Vehicles. 4 |
| ISSN: | 2574-075X |
| DOI: | 10.4271/12-04-01-0004 |
| Popis: | In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at the component level for the overall safety of the vehicle as well as the cost-saving benefits of the approach. A simple concrete automated braking example studied illustrates how separate perception system and operational design domain statistical analyses can be used to prove or disprove safety at the vehicle level. Various minor linguistic, typographical, and notational improvements. To appear in the SAE International Journal of Connected and Automated Vehicles, 4(1):2021, doi:10.4271/12-04-01-0004 |
| Databáze: | OpenAIRE |
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