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ORCIDCSCS 2022: Ingolstadt, Germany
- Björn Brücher, Christoph Krauß, Mario Fritz, Hans-Joachim Hof, Oliver Wasenmüller:
Computer Science in Cars Symposium, CSCS 2022, Ingolstadt, Germany, 8 December 2022. ACM 2022, ISBN 978-1-4503-9786-5 - Korbinian Hagn
, Oliver Grau:
Increasing pedestrian detection performance through weighting of detection impairing factors. 1:1-1:10 - Thomas Stauner, Frédérik Blank, Michael Fürst, Johannes Günther, Korbinian Hagn, Philipp Heidenreich, Markus Huber, Bastian Knerr, Thomas Schulik, Karl-Ferdinand Leiß:
SynPeDS: A Synthetic Dataset for Pedestrian Detection in Urban Traffic Scenes. 2:1-2:10 - David Tena-Gago, Qi Wang, José M. Alcaraz-Calero:
Evaluation of Level 2 Automated Driving Artificial Intelligence Readiness in Simulated Scenarios. 3:1-3:8 - Ahmed Hammam, Frank Bonarens, Seyed Eghbal Ghobadi, Christoph Stiller:
Predictive Uncertainty Quantification of Deep Neural Networks using Dirichlet Distributions. 4:1-4:10 - David Förster, Thomas Bruckschlögl, Jason Lee Omer, Tom Schipper:
Challenges and Directions for Automated Driving Security. 5:1-5:11 - Arash Vahidi, Thomas Rosenstatter, Nishat I. Mowla:
Systematic Evaluation of Automotive Intrusion Detection Datasets. 6:1-6:12 - Jingquan Ge, Yuekang Li, Yaowen Zheng, Yang Liu, Sheikh Mahbub Habib:
More Secure Collaborative APIs resistant to Flush-Based Cache Attacks on Cortex-A9 Based Automotive System. 7:1-7:9 - Oliver Pöllny, Frank Kargl, Albert Held:
Steering Your Car With Electromagnetic Fields. 8:1-8:9 - Patrick Wachter, Stephan Kleber:
Analysis of the DoIP Protocol for Security Vulnerabilities. 9:1-9:10 - Sarah Syed-Winkler, Sebastian Pape, Ahmad Sabouri:
A Data Protection-Oriented System Model Enforcing Purpose Limitation for Connected Mobility. 10:1-10:11 - Sara Ramezanian, Gizem Akman, Mohamed Taoufiq Damir, Valtteri Niemi:
Lightweight Privacy-Preserving Ride-Sharing Protocols for Autonomous Cars. 11:1-11:11 - Andrew Roberts, Mohsen Malayjerdi, Ehsan Malayjerdi, Olaf Manuel Maennel:
Combined Safety and Cybersecurity Testing Methodology for Autonomous Driving Algorithms. 12:1-12:10
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