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 follow 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.

Full Papers:

  • Submission: We are inviting industrial and academic participation in the event. We are looking for high-quality, original contributions to our peer reviewed “Full Paper” track with oral and poster presentations. The research papers must be formatted according to the acm-sigconf-authordraft template, which can be obtained from http://www.acm.org/publications/article-templates/proceedings-template.html.  Page limit is 8 pages with an additional 9th page only containing references.  Accepted papers will be published as a conference publication in the ACM Digital Library. Contributions have to be submitted in the “Full Paper” track by the deadline specified below at https://cmt3.research.microsoft.com/CSCS2018/
  • Review Process: The review process is double blind, that is, authors do not know the names of the reviewers of their papers, and reviewers do not know the authors’ names. Avoid providing information in the submission that may identify the authors in the acknowledgments where possible (e.g., company, co-workers and grant IDs). Avoid providing links to websites that identify the authors.

Extended Abstract:

  • Submission: We are inviting submissions for the “Extended abstracts” tracks with  poster presentation — with online publication (this does not count as references publication) — in the following 5 categories: demo, exhibitions, discussion papers, PhD position paper, and significant, already published work.  The extended abstracts must be formatted according to the acm-sigconf-authordraft template which can be obtained from http://www.acm.org/publications/article-templates/proceedings-template.html Page limit is 2 pages with an additional 3rd page only containing references.  Contributions have to be submitted in the “Extended Abstract” track by the deadline specified below at https://cmt3.research.microsoft.com/CSCS2018/
  • Review Process: The review process is light-weight and single blind, that is,  the authors, do not know the reviewers’ names, but the submission does not have to be anonymized

Important dates & logistics:

  • Full paper submission deadline: May 6th 2018
  • Extended abstract submission deadline: July 2nd 2018
  • Notification of acceptance (full papers): July 2nd 2018
  • Notification of acceptance (extended abstracts): July 31st 2018
  • Camera ready full papers due: July 31st 2018
  • Symposium: September 13th+14th 2018
  • Location: Munich, Germany
  • Organizers: German Chapter of the ACM

Venue:

 The symposium will take place in Munich, Germany — in proximity to the European Conference on Computer Vision. Venue will be announced soon.

Keynote Speakers:

Andrej Karpathy, Director of AI at Tesla (tentative)
Christoph Stiller, Karlsruhe Institute of Technology (KIT)
Thomas Wollinger, ESCRYPT GmbH, Germany 

Organizing Committee

Cornelia Denk, BMW, ACM SIGGRAPH Munich
Mario Fritz, MPI Saarbrücken
Oliver Grau, Intel, Germany, ACM Europe Council
Hans-Joachim Hof, TH Ingolstadt, German Chapter of the ACM
Oliver Wasenmüller, DFKI Kaiserslautern
Jürgen Pfister, BIT Technology Solutions
Björn Bücher, 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