Safe maneuverability zones & metrics for data reduction in naturalistic driving studies

Naturalistic driving studies (NDSs) capture drive data from multiple sensor modalities over long periods of time and under varying road conditions. NDS data reduction dictionaries list a range of events that are directly related to the conflicts and threat posed by the dynamics of the surrounding vehicles on the ego-vehicle. Manual reduction of such large scale data for events/conflicts related to dynamics of multiple vehicles is inefficient and prone to errors. In this paper, we present drive analysis techniques for automated NDS data reduction that can be deployed to identify, quantify and visualize threats posed to the ego-vehicle. In this regard, we propose safe maneuver zones (SMZs) that are derived based on the dynamics of surrounding vehicles with respect to the ego-vehicle. A set of metrics are formulated using the SMZs to quantify the threat posed by surrounding vehicles on the ego-vehicle. A detailed drive analysis of naturalistic driving data comprising more than 500,000 frames of data from over 5 hours of highway driving is presented. The resulting drive analysis reports characterize 7 different drives using the different metrics from the SMZs.