Methodology for identifying car following events from naturalistic data

Naturalistic Driving Studies (NDS) are becoming an integral tool for development of driver assistance systems. Because of its large volume, one challenge with working with NDS data is identifying driving scenarios of interest automatically. This study introduces a methodology for identifying situations where the driver of the instrumented vehicle applied the brakes while following another vehicle. These car following events are of interest for designers of Forward Collision Warning (FCW) systems. This algorithm could be used in conjunction with a large scale NDS, such as the Virginia Tech Transportation Research Institute´s 100-Car database, to generate population distributions of braking behavior during car following. These population distributions could be used to inform the design of warning thresholds for FCW. The heuristic algorithm developed in this study identifies car following events using forward looking radar (object range and range rate) and vehicle dynamics (speed, vehicle yaw rate). The proposed algorithm identified the same car following scenario as a visual inspection of the data in 91.8% of brake applications, suggesting it can automatically identify car following events.