Robust multi-sensor bootstrap tracking filter for quality of service estimation

This paper proposes a quality of service multi-sensor bootstrap filter for automated driving that deals with time-varying or state dependent conditions. In this way, the reliability of the sensor data fusion system is continuously evaluated in order to detect potentially dangerous conditions such as sensor failure or adverse environmental conditions such as rain and fog. Simulations show that the proposed robust multi-sensor bootstrap filter is able to robustly estimate the quality of service of the sensors. Furthermore, the filter outperforms tracking filters that assume a perfect detection profile. In addition, real world experiments in a fog simulator show that the proposed multi-sensor local-bootstrap-LMB filter outperforms all other filters in foggy conditions.

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