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A Simulation-Aided Approach to Safety Analysis of Learning-Enabled Components in Automated Driving Systems
KTH Royal Institute of Technology, Sweden.ORCID-id: 0000-0002-8028-3607
RISE Research Institutes of Sweden, Säkerhet och transport, Elektrifiering och pålitlighet. (Pålitliga transportsystem)ORCID-id: 0000-0003-4069-6252
KTH Royal Institute of Technology, Sweden.ORCID-id: 0000-0001-7048-0108
2023 (Engelska)Ingår i: Proceedings of 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Artificial Intelligence (AI) techniques through Learning-Enabled Components (LEC) are widely employed in Automated Driving Systems (ADS) to support operation perception and other driving tasks relating to planning and control. Therefore, the risk management plays a critical role in assuring the operational safety of ADS. However, the probabilistic and nondeterministic nature of LEC challenges the safety analysis. Especially, the impacts of their functional faults and incompatible external conditions are often difficult to identify. To address this issue, this article presents a simulation-aided approach as follows: 1) A simulation-aided operational data generation service with the operational parameters extracted from the corresponding system models and specifications; 2) A Fault Injection (FI) serviceaimed at high-dimensional sensor data to evaluate the robustness and residual risks of LEC. 3) A Variational Bayesian (VB) method for encoding the collected operational data and supporting an effective estimation of the likelihood of operational conditions. As a case study, the paper presents the results of one experiment, where the behaviour of an Autonomous Emergency Braking(AEB) system is simulated under various weather conditions based on the CARLA driving simulator. A set of fault types of cameras, including solid occlusion, water drop, salt and pepper, are modelled and injected into the perception module of the AEB system in different weather conditions. The results indicate that our framework enables to identify the critical faults under various operational conditions. To approximate the critical faults in undefined weather, we also propose Variational Autoencoder(VAE) to encode the pixel-level data and estimate the likelihood.

Ort, förlag, år, upplaga, sidor
2023.
Nationell ämneskategori
Teknik och teknologier Robotteknik och automation Inbäddad systemteknik
Identifikatorer
URN: urn:nbn:se:ri:diva-68139OAI: oai:DiVA.org:ri-68139DiVA, id: diva2:1816582
Konferens
26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)
Projekt
SALIENCE4CAV
Forskningsfinansiär
Vinnova, 2020-02946
Anmärkning

Funding Vinnova 2020-02946

Tillgänglig från: 2023-12-04 Skapad: 2023-12-04 Senast uppdaterad: 2024-02-02Bibliografiskt granskad

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Warg, Fredrik

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Su, PengWarg, FredrikChen, DeJiu
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Elektrifiering och pålitlighet
Teknik och teknologierRobotteknik och automationInbäddad systemteknik

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