Lucas Bublitz

Bublitz studied industrial engineering at the University of Applied Science in Munich (BEng) and the Technical University Darmstadt (MSc). Since 2020 he has been working on his PhD thesis on ensuring safety and security within AD systems for scaleability by providing an integrated process and compliance framework. In addition to his academic research, since 2018 he has worked as principal in the international technology and management consulting company P3 in the area of autonomous mobility and co-leading a team of 35 consultants for AD technology and regulation. His expertise focuses on the implementation of AD/AI regulations (UNECE).

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Presentation

Relevance of explainable AI and AI compliance for scaling AV fleets

The commercialization of AVs in San Francisco defined a new phase, as the maturity of technology is now proven at small scale. But scaling AVs requires an overall understanding of the occurrence of errors and their impact on decision making or misbehavior, as faults and failures can lead to a decrease in safety and confidence in AVs. It underlines the relevance of adopting the methods of explainable AI and applying conformity to the upcoming AI regulation. Transparency in self-driving systems ML/DL black boxes is essential for the operation and therefore the scalability of AI-enabled self-driving systems.