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Efficient Strategies for Safety Assurance of Automated Driving – half-time PhD seminar and discussion (Magnus Gyllenhammar with Prof Mario Trapp)
June 15, 2022, 13:00 – 15:00
This session takes place in conjunction with a Guest Seminar (10-11 Engineering Resilience for Cognitive Systems) and an Interactive Workshop (15.30-17.00 Holistic Technological Perspectives on Safety of Automated Driving Systems -Methods for Provision of evidence). If you are interested in joining for some or all of this Resilience Meets Assurance day, please email email@example.com to register, stating which session(s) and whether you would like to join in real life (KTH Campus) or via Zoom. (Members can accept the Outlook invitations.)
ABSTRACT: Automated Driving Systems (ADSs) promise enormous benefits to society in terms of increased comfort, safety and efficiency of the transportation system, effectively by relieving the vehicle operator from the responsibility of driving the vehicle. Contrary to previous generations of automotive systems, common development and safety assurance practises no longer suffice to accommodate the increased system complexity and operational uncertainty inherent to an ADS. This is why we have yet to see a large scale deployment of ADSs on public roads, despite recent technological progress and the promises made by several high-profile auto-makers. For that purpose, I have in my research explored the research question: What are efficient strategies for safety assurance of ADSs? In this seminar, I present my contributions to-date, wherein some insights I have gained when tackling this research question are collected. In particular, I divide my contributions into three themes: understanding the completeness of the development and verification task of the ADS; deriving a driving policy for the ADS with respect to quantitative safety requirements while incorporating knowledge from operational data, and surveying the state-of-the art, both general methods providing evidence for safety of the ADS as well as assurance methods. Leaning on these insights, I present my intended next steps of my thesis: my intended research direction and the proposed research questions to guide me through to the dissertation. More specifically, I intend to merge the existing approach of dynamic risk assessment, wherein the decisions of the ADS are conditioned on the current operating conditions; and the precautionary safety approach, where quantitative safety requirements are fulfilled through a probabilistic view of risk. Thereby, I hope to provide an assurance approach that is both effective and efficient by considering risk, from a statistical standpoint, while, in run-time, conditionally deriving appropriate actions based on the experienced operating conditions of the ADS.
The presentation will be followed by a discussion led by Professor Mario Trapp, Director of the Fraunhofer Institute for Cognitive Systems at IKS (Munich, Germany).
BIO: Magnus Gyllenhammar pursues a PhD at KTH Royal Institute of Technology as part of his employment at Zenseact in Gothenburg. His research focuses on finding efficient strategies for safety argumentation of ADSs, especially focusing on dynamic risk assessment in relation to the fulfilment of a quantitative risk norm. He received a MSc. in Engineering Physics, major in Complex Adaptive System, from Chalmers University of Technology, in 2016. In 2018 he joined Zenseact (then Zenuity) and has since worked on creating and realising data-driven strategies for verification and safety argumentation of ADSs.