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 2020 symposium is on Artificial intelligence & Security for Autonomous Vehicles.
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 for Autonomous driving
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)
- Safety and Security
We are inviting submissions for “Full Papers” and “Extended Abstracts“ in the following five categories: Demo, exhibitions, discussion papers, Ph.D. position paper, and significant, already published work. All papers must be formatted according to the acm-sigconf-authordraft template and submitted via the submission system.
- Full Papers by September 11th, 2020: Tracks with conference presentation – regular ACM online paper publication. Original work with eight to ten pages will be considered.
The review process is double-blind. Submissions have to be anonymized.
- Extended Abstracts by October 30th, 2020: Tracks with poster presentation – with online publication (not quotable as academic work). The page limit is two pages with an additional third page containing references only.
Extended abstracts in the category “significant prior published work” can be submitted “as is”.
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.