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In an automotive pre-crash application, it is vital to quickly and accurately estimate the position and velocity of objects in the frontal area of the vehicle. To improve such estimations, several radar sensors are fused to detect objects. Due to their different performance characteristics, their measurements can arrive at the pre-crash processing unit out-of-sequence. This work presents several techniques...
In this paper a new and flexible 360??-environment sensing system using short range radar sensors and active seat belt pretensioners is presented. To detect critical targets from a wide variety of closing angles, a total of only four radar sensors is used. The number of sensors can be changed, without altering the underlying sensor fusion and target detection algorithms. The challenges of pre-crash...
In this paper, the conventional JPDAM (joint probabilistic data association for merged measurements) algorithm for modelling merged observations, is applied to the automotive environment. As every sensor has limited resolution, the JPDAM should be used instead of the JPDA algorithm, which does not model the effects of measurement merging. As the JPDAM algorithm is more complicated, these effect are...
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...
In this paper, the merits of incorporating covariance propagation into a real-time Pre-Crash application are investigated. The suggested Pre-Crash algorithm activates restraint systems, such as a reversible seat belt tightening system, before an unavoidable accident happens. Sensor fusion of two short-range and one long-range radar with a target-based fusion is used to realize this vehicle safety...
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