Assessing safety functionalities in the design and validation of driving automation

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2023-09, Vol.154, p.104243, Article 104243
• Main Authors: Coppola, Angelo, D'Aniello, Claudio, Pariota, Luigi, Bifulco, Gennaro Nicola
• Format: Article
• Language: English
• Subjects: Automated vehicle system; Cooperative connected and automated mobility; Safety assessment; Testing of automated driving function; Uncertainty analysis; Vulnerable road users
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2023.104243

•Comprehensive and systematic safety assessment of Automated Driving Systems.•Assessment methodology to quantify the impact of situational parameters.•Employment of stochastic simulation and uncertainty modeling techniques.•Identification of unknown hazardous areas of operation and triggering conditions.•Comparison with traditional approaches. This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach.