16th European Dependable Computing Conference
7-10 September 2020
Munich, Germany

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Keynotes

Sep 8th, 2020:Ignacio Alvarez, Intel Corporation, LinkedIn
Sep 9th, 2020:Peter Schlicht, Volkswagen AG, LinkedIn
Sep 10th, 2020:Martin Rothfelder, Siemens AG, LinkedIn

Towards Universal Safety Guarantees of Decision Making in Automated Vehicles

Ignacio Alvarez
Senior Research Scientist, Autonomous Driving Research Lab, Intel Labs

Intel Corporation, LinkedIn

Tuesday, Sept. 8, 2020

Mass deployment of highly automated driving technology on the road requires that the industry comes to an agreement on measurement and guarantees of safety in AV decision making. This talk introduces Intel Lab’s solutions and recent technical contributions towards measurement and standardization of safety and the democratization efforts with academic, industry and regulatory communities.

Ignacio Alvarez
Ignacio Alvarez is a Senior Research Scientist at the Autonomous Driving Research Lab in Intel Labs where he develops, software, system architectures and simulation tools to accelerate the adoption of safe automated driving technologies. Previous to Intel, Ignacio worked for 8 years at BMW leading R&D and product development for Advanced Driver Assistance Systems and Vehicle Telematics Services Solutions in Europe, America and Asia. Ignacio received his International PhD in Computer Science from University of the Basque Country (Spain) and Clemson University (USA) in 2011, M.S. in Information Science in Offenburg University (Germany) and B.S. in Communication Science from Burgos University (Spain). Ignacio’s research is focused on the development of intelligent connected automated vehicles that augment human mobility with safer experiences. He has authored dozens of peer-reviewed papers, edited books on automated driving.

Challenges for Artificial Intelligence in Automated Driving

Peter Schlicht
Volkswagen AG

Volkswagen AG, LinkedIn

Wednesday, Sept. 9, 2020

After introducing the concepts for automated driving and the usage of Deep neural networks therein, we present major challenges arising from and around the application of DNNs in self driving systems.

Peter Schlicht
Peter Schlicht Peter Schlicht studied mathematics with a minor in computer science in Göttingen and received his doctorate in pure mathematics from the University of Leipzig. After a two-year research stay at the Ecole Polytechnique Fédérale (EPFL) in Lausanne (Switzerland), he joined Volkswagen Group Research in 2016 as an AI architect.
With his team, he is working on artificial intelligence for automated driving. Particularly, he is interested in methods for monitoring, explaining and robotizing deep neural networks, as well as investigating safety aspects for DNNs in automated driving systems.
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Public transport: Challenges and Opportunities for Dependability

Martin Rothfelder
Research Group Head, Siemens AG

Siemens AG, LinkedIn

Thursday, Sept. 10, 2020

Martin Rothfelder will discuss different applications of like autonomous shuttles, or ADAS-like support for train drivers. Differences to mass-products like consumer cars will be elaborated with respect to challenges for safety, reliability, and approval. How open are design domains? How to perform a Hazard Identification and Risk Analysis? How safe is safe enough? He will discuss advantages of infrastructure support for perception and options for safety cases for such approaches. Questions like: Which (A)SIL is appropriate? Is assigning ASILs appropriate? What are limitations? Challenges, advantages and limitations of methods and tools for safety cases are discussed.

Martin Rothfelder
Martin Rothfelder received his diploma in Electrical Engineering from Ruhr-University Bochum in 1991. He started as functional safety assessor for TÜV Rheinland. 1996 he joined Siemens. Now he heads the Research Group Dependability Analysis & Management. Martin Rothfelder has long-term industrial experience in safety management (rail, automotive, industrial controls) and is author of many publications in this area. His current research focusses on Model-based Reliability & Safety Engineering, and V&V of Autonomous Systems.