Abstract: In order to prevent and reduce freeway rear-end collisions, a perception model based on PC-Crash simulation and statistical analysis is proposed. With research object of basic collision model between small cars which made a high proportion of rear-end collision, parameter sensitivity of single factor and orthogonal test was analyzed based on first type of parameters in PC-Crash so as to determine main parameters of rear-end avoidance with rushing speed as index, and the top three factors that have a greater impact were reaction time, initial velocity, and deceleration. Then, a regression model based on PC-Crash simulation of 270 sets of rushing speed was established by using statistical analysis software SPSS and Stata. Finally, an optimal perception measurement model was determined with empirical evidence of typical freeway accidents. The analysis results show that the two-class Logistics regression model could determine whether following distance is safe more accurately.

Key words: freeway, rear-end perception model, PC-Crash simulation, multiple linear regression, Bayes stepwise discriminant model, two-class Logistics regression