Darmstadt University of Applied Sciences, Germany

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7th ACM Computer Science in Cars Symposium (CSCS)

CSCS is ACM’s flagship Car IT event and we welcome you to CSCS 2023.

CSCS brings together researchers, practitioners, developers, and anyone interested in solving the myriad complex problems of modern vehicles. The conference offers a common platform to discuss new developments in vehicle technology and its applications. The two main topics of the conference are Artificial Intelligence and Security for Vehicles. In addition to the presentation of current research contributions from these areas, the conference offers the opportunity for networking, joint brainstorming on current challenges and the development of new solutions.

Artificial Intelligence and Security for Vehicles

Artificial Intelligence and Security for Vehicles are both very important research areas due to the current drive in making vehicles fully autonomous and more and more connected. Computers will be responsible for handling the driving and changing conditions or environments make this very challenging and important for ensuring safe operations of driverless vehicles. However, the associated connectivity of vehicles must be secured against attackers, as successful attacks can have devastating effects.

Technology and automotive giants have vested interest and large investments to make autonomous driving commonplace. With rapid advancements in artificial intelligence and security for vehicles at so many different sources it can get hard to keep up with the state-of-the-art. Whether it is a record-breaking achievement of the world’s first “fully autonomous” taxi service or a new security protocol for over-the-air updates, the place to discuss and learn would be CSCS.


Darmstadt University of Applied Sciences is one of the largest universities of applied sciences in Germany. It is located in the City of Science Darmstadt. One of the highlights of Darmstadt is the Mathildenhöhe, which was recently recognized as a World Heritage Site by UNESCO.

Keynote Speakers

Prof. Jan Peters, Ph.D.

Technical University of Darmstadt, Germany

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt since 2011, and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) since 2022. He is also a founding research faculty member of the Hessian Center for Artificial Intelligence. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed IEEE Fellow, in 2020 ELLIS fellow and in 2021 AAIA fellow.

Despite being a faculty member at TU Darmstadt only since 2011, Jan Peters has already nurtured a series of outstanding young researchers into successful careers. These include new faculty members at leading universities in the USA, Japan, Germany, Finland and Holland, postdoctoral scholars at top computer science departments (including MIT, CMU, and Berkeley) and young leaders at top AI companies (including Amazon, Boston Dynamics, Google, and Facebook/Meta).

Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master's degrees in these disciplines as well as a Computer Science PhD from USC. Jan Peters has performed research in Germany at DLR, TU Munich and the Max Planck Institute for Biological Cybernetics (in addition to the institutions above), in Japan at the Advanced Telecommunication Research Center (ATR), at USC and at both NUS and Siemens Advanced Engineering in Singapore. He has led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007-2010) and Intelligent Systems (2010-2021).

KEYNOTE: Lessons from Robot Reinforcement Learning

Autonomous robots that can assist humans in situations of daily life have been a long-standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher-level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. To accomplish robot reinforcement learning from just few trials, the learning system can no longer explore all learn-able solutions but has to prioritize one solution over others – independent of the observed data. Such prioritization requires explicit or implicit assumptions, often called ‘induction biases’ in machine learning. Extrapolation to new robot learning tasks requires induction biases deeply rooted in general principles and domain knowledge from robotics, physics, and control. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis, juggling and manipulation of various objects.

Martin Arend

BMW Group, Germany

Since 2019 Martin Arend is General Manager Automotive Security. Part of his responsibilities are setting Automotive Security Standards, Methods, Processes, Strategy, Architecture and Functionality for the Connected Car within the BMW Group.

Martin Arend started his career as Business Development Manager in a medium-sized enterprise introducing Bluetooth Technology into client’s projects. Joining BMW Group 2003 as a Telephony/Telematics specialist he was promoted Head of CE Connectivity in 2010. 2014 he changed to the position Head of Sensors and Algorithms for Advanced Driver Assistant Systems (ADAS). 2015 he moved into the position of General Manager E/E Architecture, Technologies within BMW Research, New Technologies, Innovations, responsible for Research and Pre-Development with focus on future vehicle architectures, Enabler- and Software Technologies, including Security, in an early stage of the development process. He completed his degree in electrical engineering at Ostbayerische Technische Hochschule in Amberg/Bavaria. He holds several international patents for BMW Group.

KEYNOTE: Automotive Security Management System – State of Practice

Have you ever thought about why it is a bad idea to neglect security in vehicles? And why security and complexity never will be best friends? And how to manage this challenge in a complex vehicle anyway? Introducing what matters most from a security perspective and looking at the attackers landscape we describe some details of our security approach and provide some insights into regulation and our cyber security management system. Concluding with an outlook and the question "what's next?"

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