Human Factors of Vehicle-Based Lane Departure Warning Systems

Principal Investigator(s):

Alice Ton, Former Assistant Scientist, Mechanical Engineering


Project summary:

Run-off-road (ROR) crashes are a concern for two-lane rural and urban roadways throughout Minnesota because of the frequency by which they contribute to fatal crashes. Mitigating the severity of ROR events is an ongoing research goal to help reduce the number of ROR crashes. Examining countermeasures that may reduce ROR crashes is important for determining the most efficient and effective method of warning.

This research examined behavioral responses through the use of an in-vehicle haptic-based lane-departure warning system (LDWS) using a driving simulator. The research incorporated systematic variation to both the reliability of the warning and the 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 over-reliance 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.,/P.

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