Out-of-sequence measurement processing for an automotive pre-crash application

In this paper, the merits of incorporating out-of-sequence measurements (OOSM) into a Pre-Crash application are investigated. When an imminent front crash is detected by the Pre-Crash system, the algorithm activates a reversible seat belt tightening system. This paper points out that simple buffering is not applicable in most time critical applications such as Pre-Crash. It is crucial to have sufficiently accurate tracking information without any buffering delays, especially in urban traffic scenarios. Furthermore, the existing OOSM algorithm from Bar-Shalom [1] for the 1-step-lag case is extended to support Joint Probabilistic Data Association (JPDA). A comprehensive evaluation on simulated as well as on real sensor data is presented.