DISCUSSION ISSUES/TOPICS
Methods, Measures & Tools
- How can driver distraction be safely and rigorously studied in normal driving? How valid are studies that use test tracks, simulators, or laboratory methods?
- What measures (dependent variables) are meaningful indices of driver distraction? How do these relate to roadway safety outcomes?
- What technologies (e.g., physiological monitoring), devices (e.g., eye trackers), or analytic techniques (e.g., steering control inputs) can be used to capture measures of distraction?
- Are there good models that allow you to predict the distracting effects or crash risks associated with a particular distractor?
- What, if any, mechanisms are needed to aid in the investigation of technology related crashes and what tools are needed to support these efforts?
- What are the important unanswered questions relating to the scientific measurement of driver distraction? Where should research resources be directed?
Content Available In Each Topic Area
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 Ask the Expert |
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| Methods, Measures & Tools | ||
| Association Between Cellular-Telephone Calls and Motor Vehicle Collisions 5/18/00 10:36:46 AM
Measuring Driver Visual Distraction with a Peripheral Detection Task 5/18/00 11:12:37 AM
A Technical Platform for Driver Inattention Research 5/18/00 1:34:17 PM
The Development of a Design Evaluation Tool and Model of Attention Demand 5/18/00 1:34:25 PM
Divided Attention Ability of Young and Older Drivers 5/30/00 1:12:17 PM
Driver Workload Assessment of Route Guidance System Destination Entry While Driving: A Test Track Study 5/30/00 5:41:52 PM
Proposed Driver Workload Metrics and Methods Project 5/31/00 5:09:07 PM
Measuring distraction: the Peripheral Detection Task 6/1/00 11:58:18 AM
Need a way to track collisions where Cellular is being used. 7/5/00 2:52:06 PM
Some states do collect this data 7/6/00 9:03:20 AM
2nd and 3rd degree causes 7/8/00 7:27:54 PM
NHTSA data-base 7/9/00 8:23:25 PM
Can slow speeds cause accidents? 7/10/00 12:16:31 AM
distracting dolphins 7/12/00 11:20:43 AM
Cellular Phone Turns 7/12/00 1:36:14 PM
Cellular Phone Turns 7/12/00 1:37:15 PM
Please Explain (see full question below) 7/14/00 10:06:46 AM
Why not use horse blinders 7/18/00 3:49:28 PM
Driver responsability 7/18/00 4:30:21 PM
Measuring and Taxing the Social Costs of Distracted Drivers 7/18/00 4:32:06 PM
Accidents 7/18/00 6:20:24 PM
Driver testing 7/18/00 6:27:38 PM
Nip it in the Bud 7/18/00 8:16:16 PM
Drunk Driving Analogy 7/19/00 8:42:44 AM
Promising research direction 7/19/00 11:15:13 AM
Cell phones receiving undue criticism 7/19/00 12:04:04 PM
Responsible Drivers Need Help! 7/20/00 7:51:11 PM
Cellular Phone Turns 7/20/00 11:44:02 PM
Punishment to meet the crime 7/20/00 11:54:56 PM
Nip it in the ????? 7/21/00 12:11:20 AM
Drunk driving analogy II 7/21/00 12:20:41 AM
Promises promises 7/21/00 12:26:37 AM
Marge needs help! 7/21/00 12:34:59 AM
Reasonable assumptions 7/21/00 12:48:35 AM
Distractions 7/21/00 11:56:23 PM
Daytime Running Lights 7/26/00 7:13:04 AM
Moderator Comments and Questions 7/28/00 7:28:28 AM
Slow speed or relative speed? 7/30/00 3:59:16 PM
Criticism long overdue 7/31/00 2:35:08 AM
Primary task of driving 7/31/00 7:49:23 PM
In evaluating the safety impacts of in-vehicle technologies, what are appropriate baseline or comparative tasks? 8/1/00 1:05:43 PM Valerie Gawron Safety impacts of in-vehicle technologies installed in passenger vehicles can best be inferred from the number of near misses recorded in an instrumented vehicle. The vehicle should be dedicated to the driver who is the subject for the evaluation and the vehicle should be used as this driver's primary vehicle (e.g., fleet or personal car). The number of near misses is collected using "black boxes" installed in vehicles with ITS. The black boxes record video and performance data based on "trigger criteria." An example of a trigger criterion is vehicle deceleration greater than 0.4 g. Triggers are analyzed to determine if a near miss really occurred and what caused it. Again, a before/after comparison is made. Based on previous data, the number of triggers per number of crashes is 1000/1. At least 30,000 vehicle miles traveled are needed to derive this estimate. Note vehicles usually travel about 1000 miles per month. Alternatives to Near Misses: Braking Time & Unsafe Distances If a long period of time is not practical for the evaluation, then a short duration on-road evaluation in an instrumented vehicle or a driving simulator could be used. The data from such an evaluation, however, include the effects of learning to use both the vehicle and the in-vehicle ITS, of being watched, and of performing contrived driving scenarios. For simulators, there are also fidelity issues to consider. Data from this method include: obstacle avoidance and lane maintenance. Obstacle avoidance is measured in two ways: braking time and occurrence of unsafe distances. Olson and Sivak (1986) measured the time from the first sighting of an obstacle until the accelerator was released and the driver contacted the brake. Their data were collected in an instrumented vehicle driven on a two-lane rural road. Drory (1985) used the same measure in a simulator to evaluate the effects of different types of secondary tasks. Burger, Smith, Queen, and Slack (1977) used the brake reaction time distance between the cohort vehicle and the subject driver's vehicle. In addition they also calculated the minimum area surrounding a vehicle that should have been clear of other vehicles at the initiation of a specific maneuver and through the completion of the maneuver. This measure is similar to near misses described previously. To simplify the analysis in a later study, Burger, Mulholland, Smith, Sharkey, and Bardales (1980) used 60-foot criterion for gaps during lane changes. More recently, Korteling (1994) used car-following performance distance. In a series of on-road tests at Veridian, vehicle decelerations greater than 0.4 g were used to indicate unsafe following behavior. Measuring Lane Maintenance The risk of lane infringement and run-off-the-road accidents has been inferred from lane exceedances. This measure has already been used to evaluate in-vehicle ITS. For example, based on findings in a study of the safety aspects of Cathode Ray Tube (CRT) touch panel controls in automobiles, Zwahlen, Adams, and DeBald (1988) stated, "the probabilities of lane exceedence during the operation of a CRT touch panel (driving at 40 mph, along a straight, level, smooth roadway; under ideal driving conditions) are 3% and 15% for lane widths of 12 feet and 10 feet, respectively, which are unacceptable from a driver safety point of view." Summala, Nieminen, and Punto (1996) used lane exceedances to evaluate location of a display in an automobile cockpit. Imbeau, Wierwille, Wolf, and Chun (1989) reported that the variance of lane deviation increased if drivers performed a display reading task. The data from both these studies were collected in a driving simulator. A similar measure, Time-to-Line-Crossing (TLC), was developed to enhance preview-predictor models of human driving performance. TLC equals the time for the vehicle to reach either edge of the driving lane. It is calculated from lateral lane position, the heading angle, vehicle speed, and commanded steering angle (Godthelp, Milgram, and Blaauw, 1984). Godthelp (1986) reported, based on field study data, that TLC described anticipatory steering action during curve driving. Eye Glance Measures When data can be collected in only a single car and only on the driver (not the vehicle), glance behavior has been used to infer safety impacts. Glance duration has long been used to evaluate driver performance. For example, in an early study, Mourant and Rockwell (1970) analyzed the glance behavior of eight drivers traveling at 50 mph on an expressway. As the route became more familiar, drivers increased glances to the right edge marker and horizon. While following a car, drivers glanced more often at lane markers. Burger, Beggs, Smith, and Wulfeck (1974) discussed the importance of considering long-duration glances away from the forward scene during safety evaluations and suggested using 2.00 sec as the definition of a long-duration glance. In research more relevant to evaluating the safety impacts of in-vehicle systems, Zwahlen, Adams, and DeBald (1988), cited previously, investigated the eye scanning behavior when driving in a straight path while operating a simulated CRT touch panel display (radio and climate controls). Similarly, Imbeau, Wierwille, Wolf, and Chun (1989), also cited previously, used time glancing at a display to evaluate instrument panel lighting in automobiles. Not unexpectedly, higher complexity messages were associated with significantly longer (+0.05s more) glance times. Kurokawa and Wierwille (1991) found, in a study of control label abbreviation effects, that labels could produce small but reliable reductions in number of glances to the instrument panel. Fairclough, Ashby, and Parkes (1993) used glance duration to calculate the percentage of time that drivers looked at navigation information (a paper map versus an LCD text display), roadway ahead, rear view mirror, dashboard, left-wing mirror, right-wing mirror, left window, and right window. Data were collected in an instrumented vehicle driven on British roads. The authors concluded that this "measure proved sensitive enough to (a) differentiate between the paper map and the LCD/text display and (b) detect associated changes with regard to other areas of the visual scene" (p. 248). These authors warned, however, that reduction in glance durations might reflect the drivers' strategy to cope with the amount and legibility of the paper map. These authors also used glance duration and frequency to compare two in-vehicle route guidance systems. The data were collected from 23 subjects driving an instrumented vehicle in Germany. The data indicate, "as glance frequency to the navigation display increases, the number of glances to the dashboard, rear-view mirror and the left-wing mirror all show a significant decrease" (p. 251). Based on these results, the authors concluded, "Glance duration appears to be more sensitive to the difficulty of information update. Glance frequency represents the amount of. "Visual checking behavior" (p. 251). Differences Between Simulator and On-Road Driver Performance Olson and Sivak (1984), cited previously, used both laboratory and field studies to evaluate the effects of glare from rearview mirrors on driver performance. The laboratory study implied a reduction in seeing distance of 50% but, in the field study, the loss even at the highest glare level was only 15%. Korteling (1990) used the RT of correct responses and error percentages to compare laboratory, stationary, and on-road driving performance. RTs were significantly longer in on-road driving than in the laboratory. Summary If near misses cannot be collected then the following measures have been used to infer safety impact: braking time, distance to following vehicle, distance to obstacle, vehicle deceleration, probability of lane exceedence, and glance duration. If comparative data (i.e., in-vehicle ITS present versus absent) cannot be collected, then the following criteria have been used to infer safety impact:
What about using specific non-technology tasks as baselines to evaluate safety risks? 8/2/00 3:04:48 PM
In your opinion, what is the single most important measure for understanding driver distraction? Why? 8/7/00 8:05:29 AM
Research article on driver distraction from RoSPA 8/8/00 5:46:27 PM
comment to 'nip it in the bud' 8/8/00 5:55:08 PM
Where is the reference from the RoSPA? 8/9/00 11:28:20 AM
Mr. Murray, please 8/9/00 2:03:41 PM
I have that reference 8/9/00 2:56:15 PM
driver distraction and driver workload: not the same thing 8/9/00 3:53:52 PM
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| Research Needs | ||
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