Property Optimized GNN: Improving Data Association Performance Using Cost Function Optimization for Sensor Fusion In High Density Environments

To reliably track objects in safety critical applications such as autonomous vehicles, the chosen data association algorithm must be capable of handling scenarios where objects are in close proximity to each other and/or frequently cross paths. This paper introduces Property Optimized GNN (POG), a novel generalized property-based data association strategy to increase accuracy of track-level sensor fusion in high density scenarios. POG improves upon the Global Nearest Neighbor (GNN) strategy by accessing track properties from the previous time-step. The current implementation accomplishes this by checking if the ID of the incoming sensor track matches that of the previous confirmed track, rather than associating tracks strictly based on relative distance. To compare performance, the POG algorithm was implemented with both Euclidean and Mahalanobis distance, and compared to a GNN using the same distance equations. These algorithms are evaluated on labelled data collected from a 2023 Cadillac LYRIQ’s stock sensor suite, on a drive cycle containing both city and highway sections. The performance of each method is compared using sGOSPA metrics, accounting for location accuracy, track creation accuracy, and number of ID swaps. The results indicate that POG with Mahalanobis distance outperforms standard Euclidean and Mahalanobis association strategies on nearly all components of sGOSPA.