Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components

• URL: http://arxiv.org/abs/2311.03362v1
• Date: Thu, 28 Sep 2023 09:00:31 GMT
• Title: Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components
• Authors: Hasan Esen, Brian Hsuan-Cheng Liao
• Abstract summary: Testing, verification and validation AD/ADAS safety-critical applications remain as one the main challenges. We explain the simulation-based development platform that is designed to verify and validate safety-critical learning-enabled systems.
• Score: 0.6526824510982802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There have been major developments in Automated Driving (AD) and Driving Assist Systems (ADAS) in recent years. However, their safety assurance, thus methodologies for testing, verification and validation AD/ADAS safety-critical applications remain as one the main challenges. Inevitably AI also penetrates into AD/ADAS applications, such as object detection. Despite important benefits, adoption of such learned-enabled components and systems in safety-critical scenarios causes that conventional testing approaches (e.g., distance-based testing in automotive) quickly become infeasible. Similarly, safety engineering approaches usually assume model-based components and do not handle learning-enabled ones well. The authors have participated in the public-funded project FOCETA , and developed an Automated Valet Parking (AVP) use case. As the nature of the baseline implementation is imperfect, it offers a space for continuous improvement based on modelling, verification, validation, and monitoring techniques. In this publication, we explain the simulation-based development platform that is designed to verify and validate safety-critical learning-enabled systems in continuous engineering loops.

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