Human Factors of Vehicle-Based Lane Departure Warning Systems

Abstract

Run-off-road (ROR) crashes are a concern for two-lane rural and urban roadways throughout Minnesota due to the frequency by which they contribute to fatal crashes (Minnesota Crash Facts, 2013). Mitigating the severity of the ROR events is an on-going research goal in order to help reduce the number of ROR crashes. Examining countermeasures that may reduce ROR crashes is important to determine the most efficient and effective method of warning. Behavioral responses were examined through the use of an in-vehicle haptic-based lane departure warning system (LDWS) using a driving simulator. The study incorporated systematic variation to both the reliability of the warning and sequence of treatment conditions. An additional analysis examined the presence of behavioral adaptation after repeated exposure to the system. Severity of a ROR event was measured as the total time out of lane (TTL) and maximum lane deviation (MLD). Covariates (e.g. road shape) were examined to determine the influence they may have on the severity of a ROR. The results reveal overall LDWS efficacy. TTL was significantly longer when no system was active compared to when it was active. LDWS led to shorter duration of ROR events. Greater velocity was found to be highly predictive of longer TTL. MLD was also greater for baseline drives compared to treatment drives. No behavioral adaptation or system overreliance was detected, suggesting long term benefits of the LDWS. Drivers who actively engaged in a distraction task were at far greater risk of traveling greater and more dangerous distances out of lane.