摘要: |
This study investigated the human factors issues related to the implementation of lane departure warning systems (LDWS) to reduce side collision and run-off-road crashes for heavy trucks. Lane departures can be either intentional (e.g., to pass another vehicle or avoid an object in the roadway) or unintentional (due to drowsiness, inattention or distraction). The report discusses the recent research and applications literature that evaluates the problem of lane departure accidents and the potential for LDWS to reduce the frequency and/or severity of those accidents. The report also discusses the issues related to the use of LDWS data that are recorded to improve the fleet and individual driver safety performance. The value of systems that range from simply warning the driver, with no event recorded to the transmission of an event with the possibility of real-time intervention if driver performance is perceived to be degraded (e.g., due to fatigue or drowsiness). The study addresses the resources necessary to effectively integrate the information from these systems into the driver management system toward the goal of facilitating safe driving behaviors and reducing costly accidents. Truck accident data were analyzed to further evaluate the potential for safety benefits from LDWS. The Large Truck Crash Causation Study (LTCCS) data were analyzed with respect to the types of crashes that could be affected by LDWS (e.g., departed roadway, inattention, etc.). The analysis focused on rural highways and interstates with posted speed limits of above 50 mph. In addition, safety data for eight large commercial trucking fleets were analyzed to determine the relative frequency of accidents for which LDWS would reduce the occurrence or severity of lane or roadway departure accidents. The results indicated that, although the frequency of lane departure and run-off-road accidents was found to be relatively low, the consequences of these crashes can be very high. In addition, the relative frequency of lane departure accidents varied greatly from fleet to fleet. This indicates that the decision to implement LDWS or what type of LDWS to implement must depend upon a fleets own experience, rather than aggregate data. |