Drive analysis using lane semantics for data reduction in naturalistic driving studies

Abstract

Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analyze the visual data complemented with other sensors in the vehicle to determine critical events related to lane drifts, road departures and road delineations. To the best knowledge of the authors, this is the first work that detects and extract events listed in visual reference dictionary of NDS studies like Strategic Highway Research Program 2 (SHRP2). Detailed evaluations with real-world NDS data is presented.