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Out-of-Distribution Detection as Support for Autonomous Driving Safety Lifecycle
Semcon, Sweden.
Semcon, Sweden.
Veoneer, Sweden.
RISE Research Institutes of Sweden, Säkerhet och transport, Elektrifiering och pålitlighet.ORCID-id: 0000-0003-4069-6252
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2023 (Engelska)Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatic. )Volume 13975 LNCS, Pages 233 - 242, Springer Science and Business Media Deutschland GmbH , 2023, s. 233-242Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The automotive industry is moving towards increased automation, where features such as automated driving systems typically include machine learning (ML), e.g. in the perception system. [Question/Problem] Ensuring safety for systems partly relying on ML is challenging. Different approaches and frameworks have been proposed, typically where the developer must define quantitative and/or qualitative acceptance criteria, and ensure the criteria are fulfilled using different methods to improve e.g., design, robustness and error detection. However, there is still a knowledge gap between quality methods and metrics employed in the ML domain and how such methods can contribute to satisfying the vehicle level safety requirements. In this paper, we argue the need for connecting available ML quality methods and metrics to the safety lifecycle and explicitly show their contribution to safety. In particular, we analyse Out-of-Distribution (OoD) detection, e.g., the frequency of novelty detection, and show its potential for multiple safety-related purposes. I.e., as (a) an acceptance criterion contributing to the decision if the software fulfills the safety requirements and hence is ready-for-release, (b) in operational design domain selection and expansion by including novelty samples into the training/development loop, and (c) as a run-time measure, e.g., if there is a sequence of novel samples, the vehicle should consider reaching a minimal risk condition. [Contribution] This paper describes the possibility to use OoD detection as a safety measure, and the potential contributions in different stages of the safety lifecycle. © 2023, The Author(s)

Ort, förlag, år, upplaga, sidor
Springer Science and Business Media Deutschland GmbH , 2023. s. 233-242
Nyckelord [en]
Automated driving systems, Automotive safety, Machine learning, Out-of-Distribution detection, Safety requirements, Automation, C (programming language), Life cycle, Risk assessment, Safety engineering, Vehicle safety, Acceptance criteria, Autonomous driving, Machine-learning, Quality methods, Quality metrices, Safety lifecycle
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Datorsystem
Identifikatorer
URN: urn:nbn:se:ri:diva-64400DOI: 10.1007/978-3-031-29786-1_16Scopus ID: 2-s2.0-85152531710ISBN: 9783031297854 (tryckt)OAI: oai:DiVA.org:ri-64400DiVA, id: diva2:1755527
Konferens
29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023, Barcelona. 17 April 2023 through 20 April 2023.
Anmärkning

Correspondence Address: Thorsén, A. RISE Research Institutes of Sweden, Sweden; Funding details: Knut och Alice Wallenbergs Stiftelse; Funding text 1: This research has been supported by the Strategic vehicle research and innovation (FFI) programme in Sweden, via the project SALIENCE4CAV (ref. 2020-02946) and by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation.

Tillgänglig från: 2023-05-08 Skapad: 2023-05-08 Senast uppdaterad: 2024-04-11Bibliografiskt granskad

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Warg, FredrikThorsén, Anders

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