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LOGO www.themegallery.com Relative effects of age and compromised vision on driving performance Professor: Liu Student: Ruby.

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Presentation on theme: "LOGO www.themegallery.com Relative effects of age and compromised vision on driving performance Professor: Liu Student: Ruby."— Presentation transcript:

1 LOGO Relative effects of age and compromised vision on driving performance Professor: Liu Student: Ruby

2 Objective  To determine the relative effects of age and bad vision on driving related skills and on road accidents.

3 References  Impairments of visual, perceptual, and cognitive functions have been shown to occur with age. (Baltes, Cornelius, Spiro, Nesselroade, and Willis, 1980)  Because the older drivers have poorer perceptual and cognitive mechanical, which are the contributing factor in motor vehicle accidents. (Ball, Owsley, Sloane, Roenker, and Bruni, 1993; Coyne, Fiens, Powell, and Joslin, 1990; Odenheimer et al., 1991;…et al.)\

4 References  Except of the poor perceptual and cognitive, the sensory changes can also affect driving abilities. (Szlyk, Alexander, Severing, and Fishman, 1992; Szlyk, Fishman, Severing, Alexander, and Viana, 1993)

5 Method – participants  Control subjects-47 participants  27 people (13 female and 14 male) years.  20 people (7 female and 13 male) years.  Patients with poor vision-60 participants  37 people (18 female and 19 male) years.  23 people (7 female and 16 male) years. Disorders Macular degeneration Retinitis pigmetosa Cone-rod dystrophy Stargardt’s disease Hemianopsia Younger Older351410

6 Method – Visual field measures  Using the ∥ -4-e target measured the subjects’ visual fields.  Used two indexes of binocular visual fields:  The total extent of the visual field in degrees along the horizontal meridian to the ∥ -4-e target.  The extent of central visual field loss in degrees to the Goldmann ∥ -4-e target.

7 Method – driving assessment system  All subjects tested on an simulator, which has been described in Szlyk et al.(1992, 1993)  The subjects had to obey all traffic signs and signals on the road way.  The 15 min for the practice.  Collect data during an 8 min session.

8 Method – simulator performance indexes  Mean speed.  Braking response time to a stop sign along the simulator course.  Number of lane boundary crossings.  Variability of braking pressure.  Number of simulator accidents.

9 Method – eye and head movement analysis  The camera took the image of each subject’s face while driving.  The changing of eye and head movements in the continuation of images was the index of movement.  The image location of the center of the pupil of each eye.

10 Method – real-world accident measures & assessment of risk-taking in driving  The subjects’ self-reports about past accidents within 5 years which were analyzed.  66 subjects offer the information about the automobile accidents and violate the traffic.  All subjects were asked to respond “true” or “false” to questions for the evaluation their perceived level of risk taking during real world driving.

11 Results – performance on the interactive driving simulator  Mean speed  A significant main effect for age, F(1,99) = 15.4, p <  No significant for visual status, F(1,99) = 1.6, p =  Mean braking response time  A significant main effect for age, F(1,91) = 13.7, p <  No significant for visual status, F(1,92) = 0.89, p =  delete one subject (outlier)

12  Mean number of lane boundary crossings  A significant main effect for age, F(1,99) = 8.0, p <  No significant main effect for visual status and interaction between age and visual status.  Mean variability of braking pressure  There was no significant main effect either for age F(1,99) = 0.77, p =0.38 or visual status F(1,99) = 0.91, p =0.66.  Accident on the simulator course  A significant main effect for age, F(1,98) = 7.1, p <  No significant main effect for visual status and interaction between age and visual status. Results – performance on the interactive driving simulator

13 Result – relationships between visual status indexes and simulator indexes

14 Results – real world driving  Self-reported accidents  No significant main effect for age, F(1,102) = 2.1, p = 0.15, and visual status, F(1,102) = 0.02, p =  Averaged numbers of accidents  No significant main effect for age, F(1,62) = 0.2, p = 0.15, and visual status, F(1,62) = 0.06, p = 0.8.  State-recorded convictions  No significant main effect for age, F(1,62) =1.4, p = 0.2, and visual status, F(1,62) = 0.93, p =  There was a significant interaction between age and visual status, F(1,62) = 4.6, p < 0.04.

15 Results – compensation factors  Speed  A significant main effect for accidents showed that drivers had more accidents had higher average speeds, F(3,99) = 3.00, p< accident had an average speed of 24.9±7.5 mph 1 accident had an average speed of 27.7±8.4 mph 2 accidents had an average speed of 26.9±6.4 mph 3↑ accidents had an average speed of 34.8±5.3 mph

16 Results – compensation factors  On road risk  A little significant main effect for age, F(1,97) = 3.6, p=0.06.  A significant main effect for visual status F(1,97) = 13.1, p<  Both groups of visually poorer subjects reported taking less risk than did the older, normally sighted group.

17 Results – compensation factors  Eye movements  Vertical eye movements A significant main effect for age, F(1,65) = 6.5, p< No significant main effect for visual status and interaction between age and visual status.  Horizontal eye movements  increased as a function of age A significant main effect for age, F(1,65) = 5.09, p<0.03. No significant main effect for visual status and interaction between age and visual status.

18 Relationship of age, vision, simulator performance, and risk-taking to accidents  Using the logistic regression analyses to determine the combination of indexes (vision group status, central field loss, simulator accidents, and speed produced) that provided the most significant prediction of real world driving.  Used the self-reported accident results to do the regression analyses.

19 Relationship of age, vision, simulator performance, and risk-taking to accidents  0 is no accidents, 1is one or more accidents.  Speed and simulator accidents produced a significant, X 2 (2)=6.0, p<0.05.  Risk-taking significantly predicted membership, X 2 (2)=4.77, p<0.05.

20 Conclusion  The older drivers and all drivers with poorer vision have reduced risk-taking (not passing or not changing lanes in traffic).  All older drivers with poorer vision have increased eye movements and drive more slowly compare with younger drivers.  these behaviors was to compensate for defect in their visuocognitive and motor skills and reduce their accident risk.


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