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Visual Feature Encoding for GNNs on Road Networks
In this work, we present a novel approach to learning an encoding of visual features into graph neural networks with the application on road network data.
Learning to integrate vision data into road network data
Road networks are the core infrastructure for connected and autonomous vehicles, but creating meaningful representations for machine learning applications is a challenging task.
Landmark2Vec: An Unsupervised Neural Network-Based Landmark Positioning Method
The measurements needed for training the network are the signals observed/received from landmarks by an agent.
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