Probability estimation for an automotive Pre-Crash application with short filter settling times

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 application. A powerful, yet applicable method for using not only state but also covariance information for triggering actuators is proposed. A comprehensive parameter study on simulated as well as on real data shows statistically significant improvements in detection rate. Further, the importance of covariance errors in terms of accuracy for Pre-Crash applications is demonstrated. Even with few detection cycles and short filter settling times, a good compromise between detection rate and false alarms can be deduced.