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Approximately 28,000 crashes involving combination unit trucks occur each year when they are making lane changes, merges, or turns. One contributing factor in these crashes is inadequate visibility for truck drivers. Recent advances in video technology have heightened the prospect of improving commercial vehicle safety by improving drivers' vision around the truck. For such video systems to be implemented on heavy trucks, the systems/driver interface should be demonstrated as viable through research. This paper presents the Camera/Video Imaging Systems (C/VISs) developed at Virginia Tech Transportation Institute (VTTI), the methodology used to test them, and some results obtained.

Recently, several human factors experiments have been conducted at Virginia Tech involving both conventional and new instrument panel tasks. These experiments used drivers of all ages, thereby allowing the effects of age to be separated and examined directly. This paper reviews the age-related effects found in the experiments and shows the patterns that exist as a function of age. Recommendations are then made based on the patterns.

Eleven focus groups were conducted nationwide to gain an understanding, from the local/short haul (L/SH) drivers' perspective, of the general safety concerns related to L/SH trucking and, specifically, the degree to which fatigue plays a role. As part of the discussions, drivers listed and ranked issues that they believed caused them fatigue on the job. The top five fatigue-related issues, ranked in terms of importance, were: (1) Not Enough Sleep, (2) Hard/Physical Workday, (3) Heat/No Air Conditioning, (4) Waiting to Unload, and (5) Irregular Meal Times. Based on the results of these focus groups, it appears that Fatigue is an issue in L/SH, but perhaps not to the extent that it is in long-haul.

Assessing driver drowsiness and providing timely alerts is the basis for drowsy driver monitoring systems. Though technologies are available that claim to reliably provide this function, they tend to be single-metric systems that may not be sufficiently robust for real-world operation. To address this issue, a prototype system that integrated two drowsiness measures was developed. The prototype combined machine-vision-based drowsy driver monitoring technology and the analysis of driver/vehicle performance parameters with the goal of more reliably assessing driver drowsiness. The prototype concept, called PERCLOS+, used PERCLOS (a slow eye-closure measure) in combination with lane deviation (to assess driver performance). Based on preliminary on-road tests, the prototype was found to be more robust than a single-metric system.

This paper describes an effort to gather lane-changing and passing driver behavior data using naturalistic observation methods. Participants were ordinary commuters who drove instrumented vehicles to and from work while data were automatically collected. The three types of data included driver, vehicle response, and vehicle interaction data via collected video, vehicle sensor, and radar systems. These data were combined using a data integration system to understand lane-change and passing maneuvers. Developed specifically for this project, this system allows analysts to understand and characterize lane-changing and passing maneuvers by presenting the three types of data in an intuitive, integrated manner. The integrated data will facilitate understanding of driver behavior. This understanding will assist designers in the development of future crash reduction countermeasures including Crash Avoidance Systems (CAS).

The effects of physical motion and vehicle responsiveness on driver performance were investigated with a moving-base driving simulator. Twenty-four subjects were divided into four motion conditions ranging from no motion to roll plus yaw plus attenuated lateral translation. Each motion group drove the simulated vehicle with three levels of tire cornering stiffness. The presence of motion reduced driver control activity and path keeping deviations, but the effects of changing vehicle responsiveness were not disguised by reducing the number of motion cues. The results suggest, however, that motion cues become more important as driving maneuvers become more extreme.

This paper describes the effects of variations in vehicle response characteristics on driver-vehicle disturbance responses using a moving base driving simulator. Two exploratory studies are discussed, one dealing with vehicle transient response characteristics and the other with steady state characteristics. Close correspondence with full-scale data reported by others indicates that dynamically realistic simulators can be effective research tools. The flexibility of the simulator has facilitated the collection of other preliminary data which extend the full-scale findings. Considerably more effort will be needed, however, before strong arguments either for or against specific parameter boundaries can be made.

This paper describes in general terms the range of concerns currently on the horizon for vehicle designers, with emphasis on human factors issues. Specific new automotive technologies are taken up individually, under four major categories: Ride and Handling. Anthropometry, Instrument Panels, and Additional New Technologies. The range of concerns described shows that a great deal of careful study is needed during the last decade of this century to resolve the major human factors issues associated with automobile design.

Earlier studies have shown that drivers' visual scan patterns and dwell times are changed when using an in-car navigation display system. The fact that these changes occur raises questions about the driver's ability to adapt appropriately to high-demand driving situations. Thus, additional experiments were conducted to determine whether or not drivers adapt appropriately to high driving task demands while simultaneously navigating. One experiment was designed to investigate adaptation to high anticipated driving task demand and a second was designed to investigate adaptation to high unanticipated driving task demand. The results of the two experiments demonstrate clearly that as driving task demands increase, drivers do indeed shift their visual sampling strategy appropriately. However, variability in the data suggests that good human factors design and appropriate placement of the display remain important issues.