Development of a Human Performance Simulation Model to Evaluate In-Vehicle Information and Control Systems in Commercial Trucking Operations

The number of both crashes and fatalities involving large trucks has steadily been decreasing due to improved safety features (seat belt use, air bags, better brakes, etc.). Many safety professionals contend that to continue the improvement trend, it will be necessary to utilize “active” safety systems (e.g., adaptive cruise control, roll stability, collision warning, etc.). The passive systems are effective at reducing the severity and consequences of accidents; whereas the goal of active systems is to avoid accidents before they occur. Some of the “driver assistance” systems only provide a warning to the driver when an incident occurs while more advanced systems also record the occurrences or even provide active vehicle control (e.g., steering, braking, etc.). The proposed research specifically addresses the human factors issues associated with devices that are intended to reduce the potential of heavy truck accidents that are associated with running off the road, inadvertently moving into the adjacent lane and side collisions. Iteris, a manufacturer of a lane departure detection system states that 40% of all traffic fatalities are related to unintentional lane departure. An economic study determined that unintended land departure account for approximately 16% of total maneuver related accident costs. These personal and financial costs affect both the trucking industry and society as a whole. The Large Truck Crash Causation Study Interim Report published by the Federal Motor Carrier Safety Administration indicates that the frequency, severity and costs of these accidents are predominantly associated with human error rather than road conditions or equipment malfunction. Pomerleau et al. (1999) found that 76% of the accidents occurred on straight roads and 73% occurred when the weather was clear. The causes of lane departure and side collision accidents are most often attributed to one or a combination of three factors: fatigue; inattention; or driver distraction. Although fatigue and inattention have received the most attention, driver distraction associated with in-vehicle information systems in becoming an increasingly important issue for commercial trucking operations. The objectives of lane departure and side-collision avoidance systems are twofold: (1) reduce the number of accidents; and (2) reduce driver workload. A number of products using different technologies (optical, radar, infrared, etc.) have been developed to reduce inadvertent or inappropriate lane departures and many more are in various stages of development. The methods of altering the driver include audition (i.e., rumble strip sound) visual displays and haptic (i.e., vibration of steering wheel or seat). The criteria for when to alert the driver also varies (i.e., line crossing, time-to-line crossing, tolerance limits, etc.). Some equipment manufactures are conducting studies that collect anecdotal data that support the use of the devices based on driver surveys and testimonials. However, recent controlled studies have obtained results that are not consistent with the manufacturers’ conclusions. As with any detection system, there is a trade-off between sensitivity and the number of false alarms which can reduce effectiveness, as well as the drivers’ trust and acceptance. There is a need for independent statistically reliable and valid information that trucking organizations can use to make decisions as to the cost effectiveness of the current systems and to provide recommendations as to the characteristics of the systems that can increase their effectiveness and user/driver acceptance in operational settings. The proposed effort will address four specific issues related to driver assistance devices that are designed to reduce the frequency and severity of land departure and side collision accidents. (1) Does the use of the devices actually reduce the incidents and severity of crashes or near crashes? (2) Does the use of these devices modify driver behavior either positively (i.e., by increasing attention) or negatively (i.e., as a “crutch” that results in reduced attention)? (3) Does the effectiveness of these devices differ depending upon the characteristics of the drivers (i.e., new or experienced) or the driving scenario (long-haul vs. short-haul, flatbed, tanker, etc.)? (4) From the management standpoint, how can the data from these systems most effectively be used to positively modify driver behavior and provide user/driver acceptance of the systems?