P2LNet: HD Map Validation Using Graph Neural Networks

HD Maps are a highly important part of the autonomous driving stack to perform the activities of localization and route planning on the road. Therefore, HD Map Validation is crucial to ensure the HD Maps represent the road information as accurately as possible. While heuristics have been developed to validate HD Maps with reasonable accuracy, they are unable to solve the challenging path to traffic light validation problem of HD Map junctions. Based on the success of Graph Neural Network approaches on HD Map problems, such as Vector net, we propose the P2LNet. This P2LNet architecture consists of a fully connected subgraph followed by a Graph Encoder-Decoder Architecture that finally predicts the correct associations that would exist between paths and traffic lights in the junction. We trained and evaluated P2LNet on our in-house HD Map junction dataset, with P2LNet showing 94% accuracy on predicting the correct labels for the associations from a test set. While the results could be further improved by using edge features, P2LNet provides a significant accuracy in validating incorrect light associations. It also shows how GNN based approaches can be used to solve other significant HD Map and validation issues.