Reliable Automotive Pre-Crash System with Out-of-Sequence Measurement Processing

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 to integrate measurements into a tracking algorithm that arrive with such an out-of-sequence measurement (OOSM) scenario. A comprehensive complexity analysis of the algorithms is also presented. Most importantly, the algorithms are run on a test vehicle during real crash scenarios. The algorithms' performance is evaluated against reference data from a highly accurate laser scanner. It is shown that using advanced OOSM algorithms in pre-crash systems significantly increases performance and reduces computational cost compared to previous approaches.