Driving safety improves after individualized training: An RCT involving older drivers in an urban area

Objective: This study aimed to reproduce the results of a previous investigation on the safety benefits of individualized training for older drivers. We modified our method to address validity and generalizability issues.

Methods: Older drivers were randomly assigned to one of the 3 arms: (1) education alone, (2) education?+?on road training, and (3) education?+?on road?+?simulator training. Older drivers were recruited from a larger urban community. At the pre- and posttests (separated by 4 to 8 weeks) participants followed driving directions using a Global Positioning System (GPS) navigation system.

Results: Our findings support the positive influence of individualized on-road training for urban-dwelling older drivers. Overall, driving safety improved among drivers who received on-road training over those who were only exposed to an education session, F(1, 40) = 11.66, P = .001 (26% reduction in total unsafe driving actions [UDAs]). Statistically significant improvements were observed on observation UDAs (e.g., scanning at intersections, etc.), compliance UDAs (e.g., incomplete stop), and procedural UDAs (e.g., position in lane).

Conclusion: This study adds to the growing evidence base in support of individualized older driver training to optimize older drivers’ safety and promote continued safe driving.     

Driving difficulties as reported by older drivers with mild cognitive impairment and without neurological impairment

Objective: Considerable evidence indicates that medical conditions prevalent among older individuals lead to impairments in visual, cognitive, or psychomotor functions needed to drive safely. The purpose of this study was to explore the factors determining driving difficulties as seen from the viewpoint of 30 older drivers with mild cognitive impairment (MCI) and 30 age-matched controls without cognitive impairment.

Methods: Perceptions of driving difficulties from both groups were examined using data from an extensive questionnaire. Samples of drivers diagnosed with MCI and age-matched controls were asked to report the frequency with which they experienced driving difficulties due to functional deficits and knowledge of new traffic rules and traffic signs.

Results: The analysis revealed that 2 factors underlie MCI perceptions of driving difficulties, representing (1) difficulties associated with late detection combined with slowed response to relevant targets in the peripheral field of view and (2) difficulties associated with divided attention between tasks requiring switching from automatic to conscious processing particularly of long duration. The analysis for healthy controls revealed 3 factors representing (1) difficulties in estimating speed and distance of approaching vehicles in complex (attention-dividing) high-information-load conditions; (2) difficulties in moving head, neck, and feet; and (3) difficulties in switching from automatic responses to needing to use cognitive processing in new or unexpected situations.

Conclusions: Though both group analyses show difficulties with switching from automatic to decision making, the difficulties are different. For the control group, the difficulty in switching involves switching in new or unexpected situations associated with high-information-load conditions, whereas this switching difficulty for the MCI group is associated with divided attention between easier tasks requiring switching. These findings underline the ability of older drivers (with MCI and without cognitive impairment) to indicate probable impairments in various driving skills. The patterns of difficulties perceived by the MCI group and the age-matched healthy control group are indicative of demanding driving situations that may merit special attention for road designers and road safety engineers. They may also be considered in the design of older drivers’ fitness to drive evaluations, training programs, and/or vehicle technologies that provide for older driver assistance.     

Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Objective: We studied the changes in driving fatigue levels of experienced and inexperienced drivers at 3 periods of the day: 9:00 a.m.–12:00 p.m., 12:00 p.m.–2:00 p.m., and 4:00 p.m.–6:00 p.m.

Methods: Thirty drivers were involved in 120-min real-car driving, and sleepiness ratings (Stanford Sleepiness Scale, SSS; Hoddes et al. 1973 Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology. 1973;10:431–436.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), electroencephalogram (EEG) signals, and heart rates (HRs) were recorded. Together with principal component analysis, the relationship between EEG signals and HR was explored and used to determine a comprehensive indicator of driving fatigue. Then the comprehensive indicator was assessed via paired t test.

Results: Experienced and inexperienced drivers behaved significantly differently in terms of subjective fatigue during preliminary trials. At the beginning of trials and after termination, subjective fatigue level was aggravated with prolonged continuous driving. Moreover, we discussed the changing rules of EEG signals and HR and found that with prolonged time, the ratios of δ and β waves significantly declined, whereas that of the θ wave significantly rose. The ratio of (α + θ)/β significantly rose both before trials and after termination, but HR dropped significantly. However, one-factor analysis of variance shows that driving experience significantly affects the θ wave, (α + θ)/β ratio, and HR.

Conclusions: We found that in a monotonous road environment, fatigue symptoms occurred in inexperienced drivers and experienced drivers after about 60 and 80 min of continuous driving, respectively. Therefore, as for drivers with different experiences, restriction on continuous driving time would avoid fatigued driving and thereby eliminate traffic accidents. We find that the comprehensive indicator changes significantly with fatigue level. The integration of different indicators improves the recognition accuracy of different driving fatigue levels.     

Using a driving simulator to identify older drivers at inflated risk of motor vehicle crashes

PROBLEM: To develop appropriate assessment criteria to measure the performance of older drivers using an interactive PC-based driving simulator, and to determine which measures were associated with the occurrence of motor-vehicle crash. METHOD: One hundred and twenty-nine older drivers residing in a metropolitan city volunteered to participate in this retrospective cohort study. Using the driving simulator, appropriate driving tasks were devised to test the older drivers, whose performances were assessed by 10 reliable assessment criteria. Logistic regression analysis was then undertaken to determine those criteria that influence the self-reported crash outcome. RESULTS: As expected, driving skill of older drivers was found to decline with age. Over 60% of the sample participants reported having at least one motor-vehicle crash during the past year. Adjusting for age in a logistic regression analysis, the cognitive abilities associated with the crash occurrence were working memory, decision making under pressure of time, and confidence in driving at high speed. SUMMARY: The findings of this retrospective study indicated those individuals at inflated risk of vehicle crashes could be identified using the PC-based interactive driving simulator. Prospective studies need to be undertaken to determine whether the driving simulator can predict future crash events. IMPACT ON INDUSTRY: This study demonstrated an economical driving simulator approach to screen out problematic or unsafe older drivers before a more detailed but expensive road test is considered.