Multi-sensor fusion with out-of-sequence measurements for vehicle environment perception

Automated driving applications require an environment perception that is reliable and fast. Multi-sensor fusion is a suitable means to combine the advantages of different measurement principles. However, this may lead to out-of-sequence measurements, i.e., asynchronous measurements where the original order of the measurements is lost. High-performance out-of-sequence algorithms are therefore needed that do not depend on the order of the measurements. In addition, existence probabilities can increase the reliability of the fusion system especially in safety critical applications. This paper presents a novel approach to handle out-of-sequence measurements not only in state estimation, but also in existence estimation. The method is shown to result in equal or less computational costs than state-of-the-art methods. The proposed algorithm is evaluated with real world data from crash tests.