Generating Critical Driving Scenarios from Accident Sketches

Artificial Intelligence (AI) technologies are increasingly deployed to perform safety-critical tasks in various systems, including driverless vehicles. Therefore, ensuring the high quality of these AI-based systems is paramount to avoiding accidents and fatalities. Software testing has been proven to be a cost-effective quality assurance method for traditional software systems but requires adaptations to address the peculiarities of AI applications. For instance, thoroughly testing the software controlling autonomous vehicles requires the definition of relevant driving scenarios and their implementation in physically accurate driving simulators, which remains an open challenge. Recent work showed that simulations of critical driving scenarios such as car crashes are fundamental to generating effective test cases. However, generating such complex simulations is challenging, and state-of-the-art approaches based on natural language descriptions struggle to complete the task. Therefore, we propose CRISCE, the first approach to create accurate car crash simulations from accident sketches. Our extensive evaluation shows that CRISCE is efficient, effective, and generates accurate simulations, drastically improving state-of-art approaches based on natural language processing.