CRISCE

Abstract

Cyber-Physical Systems are increasingly deployed to perform safety-critical tasks, such as autonomously driving a vehicle. Therefore, thoroughly testing them is paramount to avoid accidents and fatalities. Driving simulators allow developers to address this challenge by testing autonomous vehicles in many driving scenarios; nevertheless, systematically generating scenarios that effectively stress the software controlling the vehicles remains an open challenge. Recent work has shown that effective test cases can be derived from simulations of critical driving scenarios such as car crashes. Hence, generating those simulations is a stepping stone for thoroughly testing autonomous vehicles. Towards this end, we propose CRISCE (CRItical SketChEs), an approach that leverages image processing (e.g., contour analysis) to automatically generate simulations of critical driving scenarios from accident sketches. Preliminary results show that CRISCE is efficient and can generate accurate simulations; hence, it has the potential to support developers in effectively achieving high-quality autonomous vehicles.