Exploring multi-stage driving behaviours prior to potential vehicle–pedestrian collisions

Abstract

A simulator-based experiment involving a vehicle–pedestrian collision scenario was designed and the corresponding temporal patterns of the multi-stage driving behaviours were then analysed using the approach of sequential pattern mining. The test scenario, conducted by 45 participants, involved a vehicle travelling along a straight two-lane roadway when an unexpected pedestrian emerges and runs across the driving lane. The whole process was partitioned into three stages – the pre-event stage, the evasive stage and the conflict stage – and several featured variables (braking reaction time, time-to-collision etc.) were collected. A sequential pattern mining algorithm was adopted to analyse the temporal relationships among these variables and, for comparison, several conventional binary regression models were also developed. The analysis revealed several sequential patterns of driving behaviours under the crash-avoidance scenario. For example, the drivers who experienced collisions were accelerating during the first stage and also had relatively long braking reaction times in the second stage. The results from the sequential pattern mining analysis indicate that the evasive stage is crucial for understanding the whole process, as it affects the influence of behaviours from the pre-event stage to the conflict stage.