Safe and effective autonomous vehicles rely heavily on advanced multi-modal perception systems. This thesis proposes the development of such a system, with a focus on explainability. The goal is to understand the system’s reasoning behind its predictions and to enhance its interpretability for various stakeholders.

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Detection using transformers can capture global context using the attention mechanisms. These architectures not only provide more concise detection results, but also allow to extract the attention weights as source of explainability. The basis of this thesis are an existing transformer-based 3D object detector for LiDAR point clouds and existing 2D object detectors for multi-view cameras.

 

I will be adding more details as the thesis during the course of the thesis.

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