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The design of a safe transportation system requires numerous design decisions that should be based on data acquired by rigorous scientific method. Naturalistic data collection and analysis methods are a relatively new addition to the engineer's toolbox. The naturalistic method is based on unobtrusively monitoring driver and vehicle performance under normal, everyday, driving conditions; generally for extended collection periods. The method generates a wealth of data that is particularly well-suited for identifying the underlying causes of safety deficiencies. Furthermore, the method also provides robust data for the design and evaluation of safety enhancement systems through field studies. Recently the instrumentation required to do this type of study has become much more cost effective allowing larger numbers of vehicles to be instrumented at a fraction of the cost. This paper will first provide an overview of the naturalistic method including comparisons to other available methods.

The TravTek vehicle provides an information-rich multifunction environment for the driver, necessitating extensive teamwork in human factors engineering the displays and controls for efficient and safe operation. Example map and text screens are presented.

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.

To better understand the issues surrounding commercial driver reliance upon in-vehicle sleeper berths for rest, ten focus groups were conducted with long-haul operators. These focus groups were held in eight cities across seven states to provide a geographically diverse sample of long-haul drivers. Issues that were explored included factors affecting the quality and quantity of sleep that drivers receive in sleeper berths, drivers' physical and mental fatigue while on the road, and other related safety issues associated with long-haul truck operations where sleeper berths are used. The results of these focus groups are presented and reflect a wide variety of driver comments, perceptions, suggestions, and recommendations.

Driver distraction, defined here as engaging in a secondary task or activity that is not central to the primary task of driving, has been shown to be a contributing factor for many crashes. The secondary tasks and other activities in which drivers choose to engage while driving is also known to be highly varied, including very complex activities(e.g., text messaging on a cellular device) to very simple activities (e.g., selecting a radio preset). Several important distinctions affect the relative risk of engaging in these tasks. Recent data from large-scale instrumented vehicle studies (i.e., “naturalistic” driving studies like the recently released “100 car study” (1)) have begun to provide data where the relative risk, in terms of crash and near crash involvement, can be directly assessed for differing secondary tasks. These data have provided some important insights into the features that create risk.

This study examined whether the effect of subsidiary tasks on driving performance can be predicted from stationary (static) testing. Alternative methods for assessing the performance of drivers during their use of in-vehicle information systems were examined. These methods included static testing in stationary vehicles, as well as dynamic, on-road testing. The measures that were obtained from static tests were evaluated in terms of how well they could predict measures obtained from driving performance during on-road testing (which included concurrent use of secondary information systems). The results indicated that measures obtained in static test settings were highly correlated with corresponding measures obtained from on-road performance testing.

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.