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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.

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.