A Hybrid Model for Safety and Security Assessment of Autonomous Vehicles

Rhea Rinaldo, Timo F. Horeis

In: 4th ACM Computer Science in Cars Symposium. ACM Computer Science in Cars Symposium (CSCS-2020) December 2-2 Feldkirchen Germany Pages 1-10 CSCS '20 4 8 ISBN 9781450376211 Association for Computing Machinery New York, USA 2020.


The competition to invent affordable, fully functional, safe and secure vehicles is driven by multiple challenges. One of the main challenge is the safety and security verification of the developed autonomous system structures. While there are many implemented strategies to ensure the safe and secure driving mission, there are only a few methods that can assess the resulting complex system structure realistically and within a reasonable time-span under consideration of the safety and security impacts. On the one hand, there are analytical approaches, e.g. Markov methods, which are often suffering from restrictive assumptions leading to worst-case assessments. As a result costly additional safety and security elements must be included to achieve the desired level of safety and security. On the other hand, numerical methods, such as Monte-Carlo simulation, can consider complex system structures and strategies but are very time-consuming, because every change of the system must be assessed by a new simulation. Consequential, the development times are increasing exponentially with every system structure update. Therefore, new approaches must be invented to support a time-efficient and realistic assessment of autonomous system structures, which includes the consideration of the intertwined dependencies and effects of safety and security. In this paper a hybrid model is presented, that combines the analytical and numerical approach to achieve a realistic assessment, while keeping the time effort reasonable. The hybrid model especially acknowledges and models the relation between safety and security, which does have a significant influence for fully autonomous vehicles.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz