Fuzzing Digital Twin With Graphical Visualization of Electronic AVs Provable Test for Consumer Safety
Yang Hong, Jun Wu
Abstract
In electronic autonomous vehicles (AVs), provable and explainable safety becomes the critical protection for their consumers. While traditional safety test schemes can detect the unsafe factors of AVs, such existing schemes still leave a number of challenges especially for provable safety test of AVs. First, existing schemes cannot continually test all traffic scenarios in the time domain such as future unknown scenarios and the scenarios with the degraded performance of the AVs. Second, it is an open issue that quantifies safety and explains the relationships between safety testing and proof, especially how safe is enough and why tests can transform into a certain level under the safety proof scale. To address these challenges, we propose a fuzzing digital twin approach, DT-FT, to construct a provable safety scheme for AVs. Specifically, we propose a dynamic strategy to guarantee the safety of AVs in the time domain and design a coverage model to quantify the safety under the safety proof scale. Moreover, we propose an approximation theory for the safety of AVs based on formal proof. Finally, a graphical visualization-based provable safety test application case for consumers is shown and the simulation results demonstrate the feasibility and effectiveness of DT-FT.