汽车工业
领域(数学分析)
集合(抽象数据类型)
计算机科学
风险分析(工程)
运输工程
工程类
业务
数学
数学分析
航空航天工程
程序设计语言
作者
Chung Won Lee,Nasif Nayeer,Danson Evan Garcia,Ankur Agrawal,Bingbing Liu
标识
DOI:10.1109/iv47402.2020.9304552
摘要
Assuring the safety of autonomous vehicles is one of the most significant challenges in the automotive industry. Tech companies and automotive manufacturers use the idea of Operational Design Domain (ODD) to indicate where their Automated Driving Systems (ADS) can operate safely. By definition from SAE J3016, an ODD defines where the ADS is designed to operate. However, it is loosely defined in no particular format, and it is unclear how exactly to formulate the ODD, which leaves it up to the ADS developer to determine. This paper proposes a methodology to identify an ODD for an ADS with statistical data and risk tolerance, where the identified ODD is constituted of a geographical map where the risk of ADS operation is lower than the pre-determined risk threshold for a given set of environmental conditions. Two different ADSs are run through this method as an example to showcase the methodology and link the identified ODD directly to the calculated performance of the ADSs. This systematically generated ODD can mitigate potential safety issues by informing the limitations of the ADS to safety drivers, through geographic and environmental boundaries.
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