The increased availability of detailed trajectory data sets from naturalistic, observational and simulation-based studies are a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and the various driving maneuvers. Despite the well-recognized research findings on both crash frequency estimation and traffic conflicts analysis carried out over the last decades, only recently researchers have started to study and model the link between the two. This link is typically made by statistical association between aggregated conflicts and crashes, which still relies on crash data and ignores heterogeneity in the estimation procedure. More recently, an Extreme Value (EV) approach has been used to link the probability of crash occurrence to the frequency of conflicts estimated from observed variability of crash proximity, using a probabilistic framework and without using crash records. In this on-going study the Generalized Extreme Value (GEV) distribution and the Generalized Pareto Distribution (GPD)-based estimation, in the peak over threshold approach, are tested and compared as EV methods using the minimum time-to-collision with the opposing vehicle during passing maneuvers. Detailed trajectory data of the passing, passed and opposite vehicles from a fixed-based driving simulator experiment was used in this study. One hundred experienced drivers from different demographic strata participated in this experiment on a voluntary base. Several two-lane rural highway layouts and traffic conditions were also considered in the design of the simulator environment. Raw data was collected at a resolution of 0.1 s and included the longitudinal and lateral position, speed and acceleration of all vehicles in the scenario. From this raw data, the minimum time-to-collision with the opposing vehicle at the end of the passing, maneuver was calculated. GEV distributions based on the Block Maxima approach and GPD distributions under the POT approach were tested for the estimation of head-on collision probabilities in passing maneuvers with different results. While the GEV approach achieved satisfactory fitting results, the tested POT underestimated the expected number of head-on collisions. Finally, the estimated GEV distributions were validated using a second set of data extracted from an additional driving simulator experiment. The results indicate that this is a promising approach for safety evaluation. On-going work of the authors will attempt to generalize this method to other safety measures related to passing maneuvers, test it for the detailed analysis of the effect of demographic factors on passing maneuvers’ crash probability and for its usefulness in a traffic simulation environment