{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:06:48Z","timestamp":1735016808443,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>Deep Neural Networks [DNNs] are being integrated into Automated Driving Systems [ADS] to perform complex perception and control problems. However, DNNs are generally challenging or impossible to interpret for the purpose of functional safety [FuSa] or Safety of the intended functionality [SOTIF] assessment. In contrast, physical models of the driving task are generally much easier to explain and assess than the abstract statistical models encoded in a DNN. In this paper, we present a statistical modelling and evaluation workflow that can be easily explained to FuSa and SOTIF assessors. Our workflow uses Bayesian networks [BN] refining fault trees and a physical model of an ADS in a given scenario. The Dominant Factors [DF] that impact the ADS risk can then be identified based on simulations of the physical model and simulations sampled from the BN. The workflow can evaluate under which conditions a tolerable risk target [TRT] can be achieved. We evaluate our proposed workflow in an example high-frequency traffic scenario, a highway cut-in scenario. We compare two methods to identify and confirm the DF for meeting the TRT. The DF found show that a static operating design domain [ODD] definition is insufficient. In the example, if the sense-plan-act control architecture is extended by a dynamic traffic monitoring protection layer, the TRT can be achieved.<\/jats:p>","DOI":"10.3233\/faia241458","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:49:22Z","timestamp":1734947362000},"source":"Crossref","is-referenced-by-count":0,"title":["Explainable Statistical Evaluation and Enhancement of Automated Driving System Safety Architectures"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8856-934X","authenticated-orcid":false,"given":"Rainer","family":"Faller","sequence":"first","affiliation":[{"name":"exida.com GmbH Munich, Germany"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241458","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:49:23Z","timestamp":1734947363000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241458","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}