Motion Prediction Methods for Surrogate Safety Analysis

Abstract

Despite the rise in interest in surrogate safety analysis, little work has been done to understand and test the impact of methods for motion prediction, which are needed to identify whether two road users are on a collision course, and to compute several surrogate safety indicators such as the time to collision. The default, unjustified method used in much of the literature is prediction at constant velocity. In this study, a generic framework is presented to predict road users' future positions depending on their current position and their choice of acceleration and direction. This method results in the possibility of generating many predicted trajectories by sampling distributions of acceleration and direction. Three safety indicators—the time to collision, an extended version of predicted post encroachment time, and a new indicator measuring the probability that the road user's attempted evasive actions will fail to avoid the collision—are computed over all predicted trajectories. These methods and indicators are illustrated in four case studies of lateral road user interactions. The evidence suggests that the prediction method based on the use of a set of initial positions seems to be the most robust. Another contribution of this study is to make all the data and code used available (the code as open source) to enable reproducibility and to start a collaborative effort to compare and improve the methods for surrogate safety analysis.