The 3rd ACM Computer Science In Cars Symposium (CSCS 2019): Future Challenges in Artificial Intelligence & Security for Autonomous Vehicles will take place this year on 08.10.2019 in Kaiserslautern at the German Research Center for Artificial Intelligence (DFKI). This year, the focus will be on artificial intelligence and IT security for autonomous vehicles. Professor Hans-Joachim Hof, Head of INSecurity – Ingolstadt Research Group Applied IT Security is currently Chair and one of the initiators of this international conference.
Tickets for participation can now be ordered under this link: Link.
Early bookingers, ACM members, members of the German Chapter of the ACM and students receive discounted admission.
Details of the event:
Aim of the event
Industry as well as academia have made great advances working towards an overall vision of fully autonomous driving. Despite the success stories, great challenges still lie ahead of us to make this grand vision come true. On the one hand, future systems have yet to be more capable of perceive, reason and act in complex real world scenarios. On the other hand, these future systems have to comply with our expectations for robustness, security and safety.
ACM, as the world's largest computing society, addresses these challenges with the ACM Computer Science in Cars Symposium. This conference provides a platform for industry and academia to exchange ideas and meet these future challenges jointly. The focus of the 2019 conference reads on AI & Security for Autonomous Vehicles.
Contributions centered on these topics are invited More information can be found at the CSCS 2019 webpage. You can also have a look at previous CSCS events.
- ARTIFICIAL INTELLIGENCE IN AUTONOMOUS SYSTEMS: Sensing, perception & interaction are key challenges — inside and outside the vehicle. Despite the great progress, complex real-world data still poses great challenges towards reliable recognition and analysis in a large range of operation conditions. Latest machine learning and in particular deep learning techniques have resulted in high performance approaches that have shown impressive results on real-world data. Yet techniques these lack core requirements like interpretability.
- AUTOMOTIVE SECURITY FOR AUTONOMOUS DRIVING: Autonomous cars will increase the attack surface of a car as they do not only make decisions based on sensor information but also use information transmitted by other cars and infrastructure. Connected autonomous cars, together with the infrastructure and the backend systems of the OEM, constitute an extremely complex system, a so-called automotive cyber system. Ensuring the security of this system poses challenges for automotive software development, secure car-to-x communication, security testing, as well as system and security engineering. Moreover, security of sensed information becomes another important aspect in a machine learning environment. Privacy enhancing technologies are another issue in automotive security, enforced by legislation, e.g., the EU General Data Protection Regulation. For widespread deployment in real-world conditions, guarantees on robustness and resilience to malicious attacks are key issues.
- EVALUATION & TESTING: In order to deploy systems for autonomous and/or assisted driving in the real-world, testing and evaluation is key. Giving realistic and sound estimates – even in rare corner cases – is challenging. A combination of analytic as well as empirical methods is required.
- General Chair: Hans-Joachim Hof, Technical University of Ingolstadt, German Chapter of the ACM
- Program Chair: Mario Fritz, CISPA, Germany
- Program Chair: Oliver Wasenmüller, DFKI Kaiserslautern, Germany
- Program Chair: Christoph Krauß, Fraunhofer Institute for Secure Information Technology (SIT)
- Bjoern Bruecher, Intel
- Oliver Grau, Intel, Germany, ACM Europe Council
- Cornelia Denk, BMW, ACM SIGGRAPH Munich Germany