Abstract
Graph Neural Networks (GNNs) have garnered substantial interest across different fields, including the automotive sector, owing to their adeptness in comprehending and managing data characterized by intricate connections and arrangements. Within the automotive realm, GNNs can be harnessed in diverse capacities to elevate effectiveness, safety, and overall operational excellence. This study is centered on the assessment of various Graph Neural Network (GNN) models and their potential performance within the automotive sector, utilizing widely recognized datasets. The objective of the study was to raise awareness among researchers and developers working on vehicle intelligence systems (VIS) about the potential benefits of utilizing Graph Neural Networks (GNNs). This could offer solutions to various challenges in this field, including comprehending complex scenes, managing diverse data from multiple sources, adapting to dynamic situations, and more. The research explores three distinct GNN models named ViG, Point-GNN, and Few-shot GNN. These models were evaluated using datasets such as KITTI, Mini Imagenet, and ILSVRC.
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