|Type: Field operational test|
|Tested system/service: Autonomous Systems|
|Countries: USA||? test users|
|? partners||? vehicles|
|Active from 10/2002 to 10/2005|
|Paul S. Rau|
|Virginia Tech. Transportation Institute|
|Data catalogue||Tools catalogue|
|Data sets used in this FOT:
||The following tools|
were used in this FOT:
DDWS: Drowsiness Detection and Warning Systems
Drowsiness has a globally negative impact on human performance by slowing response time, decreasing situational awareness, and impairing judgment. Fifty-three research questions were addressed related to performance, capabilities, acceptance, and deployment.
The FOT included control and test groups utilizing an experimental design suitable for a field test. The dataset for the analysis consisted of 102 drivers from 3 for-hire trucking fleets using 46 instrumented trucks. Fifty-seven drivers were line-haul and 45 were long-haul operators. The data set contained nearly 12.4 terabytes of video, truck instrumentation, and kinematics data for 2.4 million miles of driving and 48,000 driving-data hours recorded, resulting in the largest data set ever collected by the U.S. Department of Transportation.
- 1 Details of Field Operational Test
- 2 Financing
- 3 Cooperation partners and contact persons
- 4 Applications and equipment
- 5 Methodology
- 6 Sources of information
Details of Field Operational Test
Start date and duration of FOT execution
- Reduce the injuries, deaths, and costs associated with drowsiness.
- Develop, test, and evaluate a prototype continuous/drowsiness detection and warning system for commercial vehicle drivers.
The experimental design and data analyses tried to answer the safety benefits question of the Field Operational Test:
- What are the safety benefits associated with device usage?
- What performance and capabilities does the Drowsy Driver Warning System (DDWS) have?
- Will drivers accept the device?
- Will fleet management purchase the device?
- What are the deployment prospects of the DDWS?
Drowsiness has a globally negative impact on performance, slowing response time, decreasing situation awareness, and impairing judgment (Balkin et al., 2000; Van Dongen, Maislin, Mullington, & Dinges, 2003). A DDWS that notifies drivers to rest when they become drowsy stands to improve highway safety. The DDWS FOT investigated the effects of implementing the DFM prototype in a multitude of heavy vehicles in a real revenue producing environment. Overall, there was some evidence that the DFM prototype was successful in reducing levels of driver drowsiness. However, these findings were limited to the DFM prototype’s operating envelope, such as low luminance, speeds greater than 35 mph, drivers not wearing eyeglasses, and drivers keeping their gaze on the forward roadway. The evaluations that were performed when the conditions fell outside the operating envelope did not show significant changes in driving behavior. Drivers were not reliably found to rest sooner, change their in-vehicle behavior, or reduce their involvement in SCEs when receiving valid DFM. DDWSs must therefore address these conditions if changes in driving behavior are to occur, and an improvement in highway safety is to be observed.
When considering the operational window of the Driver Fatigue Monitor, results showed that the drivers in the Test Group had lower drowsy measurement values, and that drivers who received feedback from the system had an overall reduction of drowsy driver instances. Whereas, the experimental design was specified to support the statistical reliability of potential findings, the dataset was largely diminished from eyes-off-road time from driver distraction and normal mirror checking tasks, which were incorrectly sensed by this early prototype as drowsy episodes. As a result, no statistically reliable safety benefit was observed. However, novel data reduction procedures were able to extract data during the time periods in which the system was accurately detecting drowsiness, and analysis of these data indicated a slight reduction in critical unsafe driving events related to drowsiness. As a result, while there is some indication that a DDWS may be a promising concept, the particular prototype used in this field test to implement the concept needs significant improvement and further study.
Through this research it was expected to learn about:
- the nature of the distribution of drowsiness in the population of heavy vehicle drivers, and how these groups differ in their performance with and without the warning system;
- the effects of independent factors such as driver age, health, sleep patterns, road conditions, and type of trucking operation, etc.;
- the effect of the warning system and independent factors on conflict driving, near collisions, and severe near collisions;
- fleet acceptance and deployment prospects.
Summary, type of funding and budget
Cooperation partners and contact persons
FOT Conductor – Virginia Tech. Transportation Institute (VTTI)
FOT Independent Evaluator –Volpe Center
Main Contact person
Paul S. Rau, Ph.D., CPE National Highway Traffic Safety Administration Office of Vehicle Safety Research 400 Seventh Street, S.W. Room 6220 Washington, D.C. 20590 Tel: +1-202 366-0418 Fax: +1-202 366-7237 E-mail: prau[at]nhtsa.dot.gov
Applications and equipment
34 single-unit heavy trucks.
Equipment carried by test users
Pre-simulation / Piloting of the FOT
In October 2003, three preparatory activities were completed in advance of the FOT to verify the operational condition of the prototype equipment. The three activities included a laboratory revalidation of the Perclos metric produced by a 2nd generation Perclos monitor, the development of a Perclos system user interface suitable for commercial vehicle operations, and a study of the response characteristic of the Perclos monitor in a heavy vehicle environment. Activities addressed concerns about using the device in an operational setting. Its usability depended on the capability of the camera to detect infrared light reflected back to the source at the camera from the drivers’ retina.
Perclos revalidation was successful and involved a replication of the prospective laboratory protocol, used in two previous validation efforts. In a second effort, Attention Technologies convened focus groups separately composed of commercial drivers and design experts to determine the essential functionality of the interface. The redesign included visual displays showing the number of total lapses, the longest lapse during the previous measurement interval, and the length of roadway traversed during that lapse. Drivers would then acknowledge the lapsing by pressing a button on top of the device to silence the concurrent audible warning. Lastly, Dr. Weirwille, et al. of VTTI performed a systematic characterization study of the device detecting Perclos in trucks. The study measured the sensitivity of the device to retinal pigmentation (the ability of the eye to reflect infrared light) and to the refraction of light through eyeglasses. Sensitivity was sufficient for night-time operation with a test for retinal reflectance as a requisite for subject participation
Method for the baseline
Techniques for measurement and data collection
Recruitment goals and methods
A total of 102 drivers (101 males and one female) participated. Fifty-seven drivers were line-haul (i.e., out-and-back) operators and 45 drivers were long-haul (i.e., drivers on the road for approximately one week) operators. There were 16 weeks of data collected from each driver.
Methods for the liaison with the drivers during the FOT execution
Methods for data analysis, evaluation, synthesis and conclusions
Sources of information
|Company||Virginia Tech. Transportation Institute +|
|Contact||Paul S. Rau +|
|Ended||October 2005 +|
|Is type of||Field operational test +|
|Started||October 2002 +|
|Tested system or service||Autonomous Systems +|
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