Self-Monitored Clutter Rate Estimation for the Labeled Multi-Bernoulli Filter

Abstract

Decision making in automated vehicles is based on the environment model, which is typically computed by a tracking module from information gathered by sensors. Thus, for safe and robust operation of the vehicle, the assessment of the current quality of the tracking module is crucial. This work makes a step towards this goal by providing a clutter rate estimation method with a self-monitored quality assessment for the labeled multi-Bernoulli filter. The significance of the proposed quality index is demonstrated by comparing it with the actual estimation error calculated with ground truth data. The simulation results show that the developed quality index is a meaningful value that can be computed online without the need for ground truth data. Moreover, it is competitive and closely related to the estimation error.