LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association*

TL;DR

This paper introduces an enhanced LMB filter method that incorporates multiple hypotheses for object reference point association, significantly improving tracking accuracy in urban autonomous vehicle scenarios with occlusions.

Contribution

It extends the LMB filter to handle multiple hypotheses in measurement association, improving tracking quality and computational efficiency in complex urban environments.

Findings

Significant reduction in non-continuous tracks

Improved tracking accuracy in simulations and real scenarios

Effective early filtering of inconsistent associations

Abstract

Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required environment model for the vehicles. Previously, we have published a concept of object reference points (e.g. the corners of an object), which allows for generic sensor "plug and play" interfaces and relatively cheap sensors. This paper describes a novel method to additionally incorporate multiple hypotheses for fusing the measurements of the object reference points using an extension to the previously presented Labeled Multi-Bernoulli (LMB) filter. In contrast to the previous work, this approach improves the tracking quality in the cases where the correct association of the measurement and the object reference point is unknown. Furthermore, this paper identifies options based on physical models to sort out inconsistent and unfeasible associations at an early stage in order to keep the method computationally tractable for real-time applications. The method is evaluated on simulations as well as on real scenarios. In comparison to comparable methods, the proposed approach shows a considerable performance increase, especially the number of non-continuous tracks is decreased significantly.