Development of Standardized Descriptions of Driving Simulator Scenarios: The Older Driver 2005 TRB...

Development of Standardized Development of Standardized Descriptions of Driving Simulator Descriptions of Driving Simulator

Scenarios: The Older DriverScenarios: The Older Driver

2005 TRB Human Factors Workshop

Karlene BallUniversity of Alabama at Birmingham

Motivation

– Even though driving simulators provide a closely controlled environment for examining driving ability, few studies using simulation outcomes are consistent in assessing and reporting relevant individual characteristics that are associated with driving competence.

Background

•Individual characteristics are predictive of driving outcomes in studies of crash prediction, studies of on-the-road driving ability, and simulated driving performance; particularly among older adults. In order to develop improved comparability across simulation studies, some set of candidate characteristics should be assessed and reported.

AGEAGE

Functional impairments that affect driving skills are more prevalent in older adults. Thus age is an important individual characteristic and should be reported when presenting the results of simulator studies. Inclusion of individuals of all ages is necessary to be able to generalize results to the driving population at large.

DEMOGRAPHICSDEMOGRAPHICS

While functional characteristics may not always differ by individual characteristics such as gender, race, and education, these are important variables to include and report in driving studies due to their relationship to age, driving habits, driving environments, and at least some functional abilities.

PHYSICAL FUNCTIONPHYSICAL FUNCTION

Lower limb mobility has been found to relate to crash risk in several recent studies. This is typically measured with a timed walk measure and would be a useful assessment when older adults are included in simulated driving studies as well.

VISUAL FUNCTIONVISUAL FUNCTION

Visual function has been found to relate to driving performance in many studies. Most recently, contrast sensitivity has been found to be a valuable indicator of the potential for crash involvement. Visual acuity (commonly measured), and contrast sensitivity (at a minimum) are important individual differences variables when older drivers are assessed.

COGNITIVE FUNCTIONCOGNITIVE FUNCTION

Cognitive function is the individual differences characteristic most predictive of driving competence among older adults. This ability should be measured and reported in all simulation studies which include older individuals.

MEDICAL DIAGNOSESMEDICAL DIAGNOSES

Medical diagnoses should be reported unless individuals with any known medical conditions are specifically excluded.

MEDICATIONSMEDICATIONS

Medications known to impact driving competence should be assessed and reported unless individuals currently taking such medications are excluded from participation.

Examine the relationship between visual function, visual attention, and simulated driving performance.

•Young Adults (18-30)

•Older Adults (>65 yrs.) with good visual attention (assessed as low risk on UFOV)

•Older Adults (>65 yrs.) with poor visual attention (assessed as high risk on UFOV)

•Cognitive Function•Visual Attention

•UFOV

•Sustained Visual Attention: Starry Night

•Visual Memory•BVRT•RCFT

•Visual Search•Road Sign Test

•Auditory Attention•PASAT

•Visual Function• Contrast Sensitivity 0.5 and 1.0cpd• Pelli-Robson Contrast Sensitivity• Motion Discrimination • Distance Acuity (ETDRS)• Grating Contrast Sensitivity 22.8cpd

•Driving Simulation•Driving Conditions

•High Cognitive Demand (multiple targets)•Low Cognitive Demand (single target)

•Target Types•Originating in central vision: pedestrians, signs •Originating in periphery: passing vehicles•Critical (braking events): pedestrians, cyclists, other vehicles

•Dependent Measures•Target Response Time•Number Targets Correctly Identified

Simulator Results: Response Time to Detection in Simulator Results: Response Time to Detection in the Peripheral Field Under High Demand the Peripheral Field Under High Demand

ConditionsConditions r = 0.369, p = 0.004

UFOV TOTAL AT BASELINE

1600150014001300120011001000900800

Peripheral High Demand Reaction Time

7

6

5

4

3

2

1

0

Simulator Results: Response Time to Detection Simulator Results: Response Time to Detection in the Central Field Under High Demand in the Central Field Under High Demand

ConditionsConditions

r = 0.160, p = 0.208

UFOV TOTAL AT BASELINE

1600150014001300120011001000900800

Central High Demand Reaction Time

7

6

5

4

3

2

Simulator Results: Response Time to Detection Simulator Results: Response Time to Detection of Targets in the Peripheral Field Under Low of Targets in the Peripheral Field Under Low

Demand ConditionsDemand Conditions

r = 0.268, p = 0.055

UFOV TOTAL AT BASELINE

1600150014001300120011001000900800

Peripheral Low Demand Reaction Time

3.5

3.0

2.5

2.0

1.5

1.0

.5

Simulator Results: Response Time to Detection Simulator Results: Response Time to Detection of Targets in the Central Field Under Low of Targets in the Central Field Under Low

Demand ConditionsDemand Conditions

r = 0.035, p = 0.772

UFOV TOTAL AT BASELINE

1600150014001300120011001000900800

Central Low Demand Reaction Time

7

6

5

4

3

2

1

0

Results: Driving Simulation Measures

0

1

2

3

4

5

6

7

Seconds

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Percent Correct

Young Adults

Older Aged w/ goodUFOVOlder Aged w/ poorUFOV

Response Time (seconds) Percent Correct

*

*

**

*

*

Center Targets Under High Demand Conditions

CutpointsCutpoints

Using cutpoints as grouping variables is frequently valuable if there is some standard for differentiating high and low risk drivers.

Simulation and Older AdultsSimulation and Older Adults

Individual Characteristics related to Risk– Distraction– Speed of Processing– Divided Attention– Mental Status

Intervention Studies

Particularly valuable for standardizing pre-post comparisons