Safety of the Intended Functionality Validation for Automated Driving Systems by Using Perception Performance Insufficiencies Injection

Author:

Expósito Jiménez Víctor J.1ORCID,Macher Georg2ORCID,Watzenig Daniel13ORCID,Brenner Eugen2

Affiliation:

1. Virtual Vehicle Research GmbH, 8010 Graz, Austria

2. Institute of Technical Informatics, Graz University of Technology, 8010 Graz, Austria

3. Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Techhnology, 8010 Graz, Austria

Abstract

System perception of the environment becomes more important as the level of automation increases, especially at the higher levels of automation (L3+) of Automated Driving Systems. As a consequence, scenario-based validation becomes more important in the overall validation process of a vehicle. Testing all scenarios with potential triggering conditions that may lead to hazardous vehicle behaviour is not a realistic approach, as the number of such scenarios tends to be unmanageable. Therefore, another approach has to be provided to deal with this problem. In this paper, we present our approach, which uses the injection of perception performance insufficiencies instead of directly testing the potential triggering conditions. Finally, a use case is described that illustrates the implementation of the proposed approach.

Funder

Graz University of Technology

Publisher

MDPI AG

Reference64 articles.

1. (2022). Commission Implementing Regulation (EU) 2022/1426—Commision Implementing Act AD v4.1 (Standard No. (EU) 2022/1426).

2. National Transportation Safety Board (NTSB) (2023, May 12). Collision between a Sport Utility Vehicle Operating with Partial Driving Automation and a Crash Attenuator, Mountain View, California, March 23, 2018, Available online: https://www.ntsb.gov/investigations/AccidentReports/Reports/HAR2001.pdf.

3. Bonnefon, J.F. (2021). 18 The Uber Accident. The Car That Knew Too Much: Can a Machine Be Moral?, The MIT Press.

4. Shah, S.A. (2019). Safe-AV: A Fault Tolerant Safety Architecture for Autonomous Vehicles. [Ph.D. Thesis, McMaster University].

5. AI Incident Database (2024, March 03). Incident 293: Cruise’s Self-Driving Car Involved in a Multiple-Injury Collision at an San Francisco Intersection. Available online: https://incidentdatabase.ai/cite/293/.

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