Functional Safety Verification for Autonomous UGVs-Methodology Presentation and Implementation on a Full-Scale System

Autonomous vehicles (AVs) are expected to be on public roads in the near future, but some critical aspects of this reality are yet to be resolved. One of these aspects is the lack of a sufficient safety-performance-verification technique. The existing tools of functional-safety engineering do not provide a comprehensive solution for the verification of artificial intelligence and machine learning. For the verification of this type of algorithms, it has become widely accepted in the recent years that simulation tests should be used. Nevertheless, to the best of our knowledge, no detailed method for how these simulation tests should be performed has yet been suggested. To start tackling this gap, this paper presents a verification methodology based on the statistical testing of AVs' safety-related functionality in simulated scenarios. Also, presented here is a test-case implementation of the methodology on a full-scale autonomous unmanned ground vehicle (UGV). In this example, the functionality of safe autonomous off-road navigation is verified. It is shown that the statistical performance data that are gathered using simulation can be used to accurately predict the overall safety performance of UGVs in real life. Our intention is that this paper will provide a starting point for the further discussion on the exact way simulation tests shall be used for the safety verification of AVs.