A Variational Deep Synthesis Approach for Perception Validation

Abstract

AbstractThis chapter introduces a novel data synthesis framework for validation of perception functions based on machine learning to ensure the safety and functionality of these systems, specifically in the context of automated driving. The main contributions are the introduction of a generative, parametric description of three-dimensional scenarios in a validation parameter space, and layered scene generation process to reduce the computational effort. Specifically, we combine a module for probabilistic scene generation, a variation engine for scene parameters, and a more realistic sensor artifacts simulation. The work demonstrates the effectiveness of the framework for the perception of pedestrians in urban environments based on various deep neural networks (DNNs) for semantic segmentation and object detection. Our approach allows a systematic evaluation of a high number of different objects and combined with our variational approach we can effectively simulate and test a wide range of additional conditions as, e.g., various illuminations. We can demonstrate that our generative approach produces a better approximation of the spatial object distribution to real datasets, compared to hand-crafted 3D scenes.