2^nd ACM Computer Science in Cars Symposium (CSCS 2018)

FUTURE CHALLENGES IN ARTIFICIAL INTELLIGENCE & SECURITY FOR AUTONOMOUS VEHICLES

cc-by-nc-nd (Frank Fujimoto)

The symposium took place in Munich, Germany — in proximity to the European Conference on Computer Vision as a satellite event. 

Scope:

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 to be yet more capable to 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 2018 conference lies on AI & Security for Autonomous Vehicles. Contributions centered on these topics are invited.

Topics:

Submission of contributions are invited in (but not limited to) the following key areas:

• 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 these techniques lack core requirements like interpretability.
• Automotive Security for Autonomous driving: Autonomous cars will increase the attack surface of a car as they 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

Program

September 13th: Security

All dates are UTC+1 / CET

September 14th: AI

All dates are UTC+1 / CET

Keynote Speakers

• Responsibility Sensitive Safety
  Jack Weast, Chief Systems Architect for Automated Driving Solutions, Intel

• The art of cyber security orchestration
  Dr. Thomas Wollinger since 2007 Managing Director ESCRYPT GmbH.

• Learning to Drive by Learning to See
  Andreas Geiger, University of Tübingen, Max Planck Institue for Intelligent Systems 

• A future with affordable self-driving vehicles
  Raquel Urtasun, Uber ATG, Vector Institute, University of Toronto

Conference Committee

General Chair

• Oliver Grau, Intel, Germany, ACM Europe Council

Program Chairs

• Hans-Joachim Hof, TH Ingolstadt, German Chapter of the ACM

• Mario Fritz, CISPA Helmholtz Center i.G.

Organizing Committee

• Cornelia Denk, BMW, ACM SIGGRAPH Munich

• Oliver Wasenmüller, DFKI Kaiserslautern

• Jürgen Pfister, BIT Technology Solutions

• Björn Brücher, Intel

Confirmed Program Committee

• Ali Al-Bayatti, De Montfort University, Leicester, UK

• Björn Andres, Bosch, Germany

• Rodrigo Benenson, Google, Switzerland

• Chih-Hong Cheng, Fortiss, Germany

• Trevor Darrell, UC Berkeley, USA

• Gareth Davies, University of South Wales, UK

• Lipika Deka, De Montfort University, Leicester, UK

• Alexey Dosovitskiy, Intel, Germany

• Markus Enzweiler, Daimler, Germany

• Uwe Franke, Daimler, Germany

• Andreas Geiger, MPI Intelligent Systems, Germany

• Rudolf Hackenberg, OTH Regensburg, Germany

• Helge Janicke, De Montfort University, Leicester, UK

• Christoph Krauß, Fraunhofer SIT

• Antonio Lopez, UAB, Spain

• Tilo Müller, Friedrich-Alexander Universität Erlangen-Nürnberg, Germany

• Andrey Nikishin, Kaspersky Lab, UK

• Stefan Nürnberger, CISPA, Germany

• Sebastian Renner, OTH Regensburg, Germany

• Arvid, Rosinski, Audi, Germany

• Bernt Schiele, MPI Informatics, Germany

• Vitaly Shmatikov, Cornell, USA

• Philipp Slusallek, DFKI, Germany

• Didier Sticker, DFKI, Germany

• Guangzhi Qu, Oakland University, USA

• Armin Wasicek, UC Berkeley, USA

• Nils Weiß, OTH Regensburg, Germany

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