Risk quantification for automated driving systems in real-world driving scenarios
Number of pages
SourceIEEE Access, 9, (2021), pp. 168953-168970
Article / Letter to editor
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SW OZ DCC AI
SubjectCognitive artificial intelligence
The development of safety validation methods is essential for the safe deployment and operation of Automated Driving Systems (ADSs). One of the goals of safety validation is to prospectively evaluate the risk of an ADS dealing with real-world traffic. ISO 26262 and ISO/DIS 21448, the leading standards in automotive safety, provide an approach to estimate the risk where the former focuses on risks due to potential malfunctioning of components and the latter focuses on risks due to possible functional insufficiencies. The main shortcomings of the approach provided in ISO 26262 are that it depends on subjective judgments of safety experts and that only a qualitative risk estimation is performed. ISO/DIS 21448 addresses these shortcomings partially by providing statistical methods to guide the safety validation, but no complete method is provided to quantify the risk. The first objective of this article is to propose a method to estimate the risk of an ADS in a more quantitative and objective manner. A data-driven approach is used to rely less on subjective judgments of safety experts. The output of the method is the expected number of injuries in a potential collision. Thus, the method is quantitative, the result is easily interpretable, and the result can be compared with road crash statistics. The second objective is to provide a method that supports the risk assessment as stipulated by the ISO 26262 and ISO/DIS 21448 standards by decomposing the quantified risk into the 3 aspects of risk as mentioned in these standards: exposure, severity, and controllability. The proposed methods are illustrated by means of a case study in which the risk is quantified for a longitudinal controller in 3 different types of scenarios. The code of the case study is publicly available.
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