The present application belongs to the technical field of automatic
driving test, and particularly relates to an automatic driving
perception system edge scene generation method based on a
diffusion model, a natural driving scene image set and an
edge element set are constructed, and key
prior information such as a road
mask and depth is extracted; a text controllable
diffusion model is trained and adapted using the natural driving scene set to learn the natural driving scene distribution; a multi-
label discriminant sub-model is trained based on the
edge element set to output calibrated element confidence; in the sampling stage, text
semantics and elements are fused, and regionalization injection is carried out under structural prior constraints to guide the sampling process; geometric and physical consistency
verification and automatic screening form a
closed loop to obtain high-fidelity edge scene image data. The present application can realize efficient testing of the performance of an automatic driving
perception system, quickly excavate functional defects of the
system, and accelerate the industrialization of automatic driving cars.