Abstract
Investigating crash progression through naturalistic driving studies (NDS) could give valuable insights in crash causation analysis and thus, benefit crash prevention. This study utilises NDS data from the Strategic Highway Research Program 2 (SHRP2 NDS data) to look into the whole crash sequence, from a normal driving situation until a crash or a near-crash event. The objectives are to explore vehicle kinematics before the event, investigate the feasibility of crash risk indicators to detect the early stages of crash development and further examine the factors affecting Time To Collision (TTC) values during the crash sequence. An empirical approach and a multilevel mixed effects modelling technique were followed. The results reveal that longitudinal acceleration, lateral acceleration and yaw rate can be reliable indicators for detecting deviations from normal driving. Moreover, TTC values are affected by vehicle type, speed of the ego vehicle, longitudinal acceleration and time within the crash sequence. The model indicates a timestamp where a detectable reduction in TTC values occurs, which could be a first step towards more effective Advanced Driver Assistance Systems (ADAS) aiming to halt early deviations before they evolve to mishaps.
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