Exploration of Diffusion-Based Test Case Generation of Autonomous Driving Systems

Abstract

Autonomous driving systems demand extensive testing to guarantee safety and reliability. However, real-world testing is often costly and limited in variety. This paper investigates the use of diffusion models to generate synthetic driving images as a means of augmenting test datasets for autonomous driving systems. Leveraging their capability to produce diverse and highly realistic images, diffusion models can simulate various driving conditions, including rare or challenging scenarios that are difficult to replicate. By incorporating these synthetic images into the testing pipeline, self-driving systems can be assessed across a wider range of conditions, enhancing their robustness and safety. © 2025 IEEE.