Track-to-track Association based on Stochastic Optimization

Multi-sensor fusion can improve environment perception, e.g., by increasing the field of view in collective perception, where intelligent vehicles communicate. Track-to-track fusion in a collective perception scenario makes it necessary to associate tracks from multiple sensors. Especially in settings with many sensors that have limited field of view, track-to-track association can be quite challenging. In this work, we develop a stochastic optimization-based approach for an arbitrary number of sensors with a limited field of view, which utilizes a cluster likelihood to sample joint associations. The proposed method clearly outperforms a greedy approach and computes the most likely associations with only a few samples. We evaluate the approaches on simulated data in static and dynamic scenarios.