Emergence of a Cognitive Car-Following Driver Model: Application to Rear-End Crashes with a Stopped Lead Vehicle

Abstract

Rear-end crashes are a major roadway safety problem, and the potential of crash countermeasures to address this has long been recognized. High-frequency or severe-consequence scenarios are focused on the general lead-vehicle-not-moving (LVNM) case and specific crash scenarios. Operating scenarios are identified, and frequencies are assessed. From these, a small number of prevalent LVNM crash scenarios are identified as the focus for subsequent model development and crash counter-measure efforts. These scenarios suggest nominal atmospheric, roadway, lighting, vehicle, and driver conditions in designing cost-effective safety features to avoid LVNM rear-end crashes. From this, emergent models for cognitive car following are developed, based on fusing current knowledge. This will serve as a foundation for further model development efforts as well as for future human-factors experiments.