Submissions

We are inviting submissions for “Full Papers” and “Extended Abstracts”. All papers must be formatted according to the acm-sigconf-authordraft template and submitted via the submission system.

• Full Papers: Track with conference presentation and regular ACM online paper publication. We are looking for papers with high quality, original and unpublished contributions of eight to ten pages (without references). The review process is double-blind. Submissions have to be anonymized.

• Extended Abstracts: Track with poster presentation and online publication (not quotable as academic work). The page limit is two pages with an additional third page containing references only. 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.

If you have any questions about the submission, please drop an email to

Important Dates

All submissions must be received by 11:59 p.m. anywhere-on-earth time on the day of the corresponding deadline.

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 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 (CSCS). This symposium provides a platform for industry and academia to exchange ideas and meet these future challenges jointly. The focus of the 2023 symposium is on Artificial intelligence & Security for Autonomous Vehicles.

Symposium Scope

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 operating conditions. Latest Machine Learning and in particular Deep Learning techniques have resulted in high-performance approaches that have shown impressive results.

• Robust algorithms for semantic, geometric and dynamic perception around the vehicle
• Driver and interior monitoring
• Interpretable and explainable Deep Neural Networks
• Datasets and benchmarks with real and/or synthetic data as well as novel evaluation strategies
• Fusion of cameras with active sensors, such as LiDAR, Radar, etc.
• Embedded optimization of Deep Neural Networks as well as run-time optimizations

Automotive Security

Information technology enables connected autonomous vehicles and many new applications but also introduces new threats. For example, an attacker could manipulate safety-critical systems such as the braking system to endanger the live and limb of passengers and other road users. An attacker could also try to generate movement or behavior profiles of the vehicle user. Thus, ensuring security and privacy is of paramount importance but poses several challenges. CSCS is a forum for discussing the latest developments in the context of security and privacy for autonomous vehicles and bringing together researchers and practitioners.

• Secure in-vehicle communication and lightweight cryptographic protocols
• System security of electronic control units, gateways etc. (e.g., hardware security, OS security)
• Secure external communication (e.g., V2X, Internet) and protocols (e.g., secure remote update)
• Automotive security standardization, secure software development, security testing
• Automotive Security Monitoring (e.g., IDS, IPS, SIEM, SOC)
• Privacy-enhancing technologies and mechanisms for automotive applications
• Piracy, product counterfeit, and theft protection (e.g., RKE, Immobilizer)
• Practical Security Evaluations / Penetration Testing
• Safety and Security

Camera-Ready and Conference Presentation

If a paper is accepted, the author list of the initial submission cannot be changed when preparing the camera-ready version. Authors of accepted papers must also guarantee that their papers will be presented at the conference. At least one author of the paper must be registered at the appropriate full conference rate. Please also consider the following point:

• By submitting your article to an ACM Publication, you are hereby acknowledging that you and your co-authors are subject to all ACM Publications Policies, including ACM’s new Publications Policy on Research Involving Human Participants and Subjects. Alleged violations of this policy or any ACM Publications Policy will be investigated by ACM and may result in a full retraction of your paper, in addition to other potential penalties, as per ACM Publications Policy.
• Please ensure that you and your co-authors obtain an ORCID ID, so you can complete the publishing process for your accepted paper. ACM has been involved in ORCID from the start and we have recently made a commitment to collect ORCID IDs from all of our published authors. The collection process has started and will roll out as a requirement throughout 2022. We are committed to improve author discoverability, ensure proper attribution and contribute to ongoing community efforts around name normalization; your ORCID ID will help in these efforts.

Program Committee

• Apratim Bhattacharyya, Qualcomm AI Research
• Dennis Kengo Oka, Synopsys
• Florian Fenzl, Fraunhofer SIT
• Frank Kargl, University of Ulm
• H. Gregor Molter, Porsche AG
• Kevin Gomez, Technische Hochschule Ingolstadt
• Marc Stöttinger, RheinMain University of Applied Science
• Markus Enzweiler, Esslingen University of Applied Sciences
• Markus Tschersich, Continental Automotive Technologies GmbH
• Mert D. Pesé, Clemson University
• Philipp Heidenreich, Opel Automobile GmbH
• René Schuster, DFKI
• Sheikh Mahbub Habib, Continental Automotive Technologies GmbH
• Stefan Katzenbeisser, University of Passau
• Stefan Milz, Spleenlab.ai / Ilmenau University
• Tim Fingscheidt, Technische Universität Braunschweig
• Tim Leinmüller, Denso
• Timo Saemann, Valeo
• Timo van Roermund, NXP
• Tobias Eggendorfer, Technische Hochschule Ingolstadt