A Methodology for Model-Based Validation of Autonomous Vehicle Systems

The deployment of autonomous vehicles requires safety assurance and performance guarantees of the developed system. However, this is complex due to the number of scenario variations and uncertainty associated with the operating environment. To alleviate this challenge, we propose a model-based validation methodology that relies on a functional hierarchy for the breakdown and simplification of the system navigation functions, and the Backtracking Process Algorithm to identify, trace, and probabilistically quantify risk significant event sequences (scenarios) that lead to Top Events of interest (such as requirement violations). This methodology is demonstrated on a scenario with an occluded pedestrian crossing the road. We are able to identify risks associated with the actor classification problem and sudden changes in behavior of the pedestrian.