Steve Dias Da Cruz

"Development and Evaluation of Deep Learning Methodologies for Safety Critical Sensor Applications in the Automotive Industry"

For most automotive applications the needed training data for deep learning methods imply very high measurement and annotation efforts. Deep neural networks (DNN) trained in a single environment take non-relevant characteristics in an uncontrolled way into account and therefore data must be recorded repetitively for different environments. Consequently, the available means to reduce required training efforts are limited. This project will investigate and develop methods for invariant-salient information separation which will improve the robustness and invariance of DNNs to changes irrelevant to the application problem. The efficiency of the resulting background invariant DNN will be tested on a camera system in the vehicle interior to classify and detect occupancy and passengers.In this talk I will introduce the challenges for the vehicle interior regarding generalization and robustness of machine learning models. I will present SVIRO, a recently released synthetic dataset for the vehicle interior accepted for publication at WACV'20. Finally, I will talk about future investigations regarding autoencoders and disentangled latent space representations to mitigate the aforementioned challenges.