Crash Probability and Error Rates for Head-On Collisions Based on Stochastic Analyses

Active safety systems are developed in the automotive industry to help avoid or mitigate collisions. To develop collision-avoidance or mitigation systems, an appropriate lead time must be determined to provide a warning or action with acceptable false positive and negative rates. There has been much research on the lead time for the rear-end collision, but the lead time for the head-on collision has not been studied much because of the complexity of the loadcase. In this paper, the crash probabilities of the head-on collision were estimated, and adaptive lead times were proposed. In addition, false positive and false negative rates were assessed for some precrash sensor errors. For the assessment, an analytical vehicle model was validated against static and dynamic test data, and the driver's behaviors in normal and evasive maneuvers were surveyed and modeled. Using the analytical vehicle model and the driver models, stochastic analyses were conducted to assess the crash probability, the adaptive lead times, and the error rates.