Hybrid approach merging classical Bayesian tracking with learned neural fusion achieves production-grade performance without requiring dense annotations, making it practical for autonomous vehicle systems.
LEO combines graph neural networks with Bayesian tracking to estimate the shape and trajectory of vehicles for autonomous driving. It fuses data from multiple sensors (radar, lidar, camera) to track complex objects like articulated trucks while remaining computationally efficient for real-time use.