Multi-sensor multi-object tracking in a track-to-track fusion framework involves the grouping of tracks (from different sensors) that belong to the same perceived object. In particular for collective perception scenarios in large-scale traffic systems the number of sensors and objects can be huge, as a large number of vehicles can be equipped with multiple sensors. In order to cope with the intractable number of possible associations, recently a stochastic optimization approach for track-to-track association was proposed. The key idea is to successively improve an initial association by means of performing random modifications, i.e., actions, on the current association. In this work, we develop a novel deterministic version of the algorithm, which employs herding in order to deterministically choose the next action. Simulations demonstrate that the deterministic version of stochastic optimization provides comparable results to the stochastic version with a significantly lower variance.

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.

In the case of high-resolution or near field sensors, an object normally gives rise to multiple measurements per scan. One of the key tasks in tracking such objects is to differentiate the origins of the measurements. In this work, a new data association approach for extended object tracking, which is inspired by Joint Integrated Probabilistic Data Association (JIPDA), is proposed. The key idea is to calculate marginal association probabilities for individual measurements (instead of considering measurement partitions). Our problem formulation allows us to obtain the marginal association probabilities without collective exhaustion of association hypotheses and partitions. The proposed data association method is illustrated first using a simulation with Gaussian distributed measurements. Combined with an extended object measurement model, the data association quality is further assessed in a simulation and an experiment by tracking pedestrians using Lidar data from the KITTI dataset.