elderly drivers
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Abstract The development of highly automated vehicles (HAV) can meet elderly drivers’ mobility needs; however, worse driving performance after a takeover request (TOR) is frequently found, especially regarding non-driving related tasks (NDRTs). This study aims to detect the correlation between takeover performance and underlying cognitive factors comprising a set of higher order cognitive processes including executive functions. Thirty-five young and 35 elderly participants were tested by computerized cognitive tasks and simulated driving tasks to evaluate their executive functions and takeover performance. Performance of n-back tasks, Simon tasks, and task switching were used to generate updating, inhibition, and shifting components of executive functions by principal component analysis. The performance of lane changing after TOR was measured using the standard deviation of the steering wheel angle and minimum time-to-collision (TTC). Differences between age groups and NDRT engagement were assessed by two-way mixed analysis of variance.Older participants had significantly lower executive function ability and were less stable and more conservative when engaged in NDRT. Furthermore, a significant correlation between executive function and lateral driving stability was found. These findings highlight the interaction between age-related differences in executive functions and takeover performance; thus, provide implications for designing driver screening tests or human-machine interfaces.

Zusammenfassung. Altern geht mit kognitiven und nicht-kognitiven Veränderungen einher, die bei einem relevanten Anteil älterer Menschen Risikofaktoren für eine Reduktion der Fahrsicherheit darstellen. Da praktische Fahrverhaltensbeobachtungen aufwendig und kostenintensiv sind, besteht ein zunehmender Bedarf an validen Screeningverfahren, die eine Erfassung dieser Risikofaktoren ermöglichen und eine diagnostisch genaue sowie zeitökonomische Einschätzung der Fahrsicherheit älterer Kraftfahrerinnen und Kraftfahrer gewährleisten. Unsere Arbeitsgruppe hat sich daher in den letzten Jahren mit der Entwicklung und Validierung eines multifaktoriellen Screeningverfahrens beschäftigt. In diesem Beitrag werden Konstruktion, eine erste Validierung sowie Angaben zur praktischen Anwendung des neuen Verfahrens „Seniorenberatung Aufgrund Fahreignungsrelevanter Einschränkungen – revidierte Fassung“ (engl.: Safety Advice For Elderly drivers – revised version) (SAFE-R) vorgestellt. Der SAFE-R ermöglicht die überprüfung von 11 evidenzbasierten fahrsicherheitsrelevanten Risikofaktoren bei älteren Kraftfahrer_innen mit und ohne leichte kognitive Beeinträchtigung. In einer On-Road-Studie an 74 älteren Menschen mit und ohne leichte kognitive Beeinträchtigung konnte der SAFE-R mit einer Sensitivität von 95 % und einer Spezifität von 75 % zwischen fahrsicheren und fahrunsicheren Kraftfahrer_innen differenzieren. Der SAFE-R stellt somit ein valides und zugleich ökonomisches Instrument zur Einschätzung der Fahrsicherheit von Senior_innen mit und ohne leichte kognitive Beeinträchtigung dar.

Variable message signs (VMS) are used to display messages providing up-to-date traffic-relevant information so that drivers can safely adapt their behavior in real time. The information reported in a VMS should be brief but comprehensive to minimize perception time. The latter can be influenced by the way the message is displayed. This study investigates how the different ways of displaying the same message can influence reading time and the information perception process at different driving speeds. Specifically, the following message characteristics are investigated: (i) use of uppercase and lowercase letters; (ii) use of familiar pictograms; and (iii) use of less familiar pictograms. Furthermore, as perception time typically changes with ageing, drivers belonging to three different age classes are tested. The experimentation was performed by simulating a vehicle passing along a straight road upon which a VMS displaying different messages was placed. Experimentation results are analyzed using the Kruskal–Wallis test, Friedman rank-sum test and Welch one-way ANOVA, showing that: (i) the use of uppercase or lowercase does not seem to significantly affect reading times; (ii) the use of pictograms that are not very familiar to habitual road-users can be counterproductive for the perception process; (iii) elderly drivers always have greater difficulty in perceiving the message than young or middle-aged drivers. The findings of this study can be of help for traffic authorities to design the most suitable structure for a VMS so that its information can be unequivocally and immediately conveyed to drivers.

Age-related decline in lower limb motor control may cause errors in pedal operation when driving a car. This study aimed to clarify the kinematics and electrophysiological characteristics of the pedal-switching operation associated with emergency braking in the case of elderly drivers. The participants in this study consisted of 11 young drivers and 10 elderly drivers. An experimental pedal was used, and the muscle activity and kinematic data during braking action were analyzed using the light from a light-emitting diode installed in the front as a trigger. The results showed that elderly drivers took the same time from viewing the visual stimulus to releasing the accelerator pedal as younger drivers, but took longer to switch to the brake pedal. The elderly drivers had higher soleus muscle activity throughout the process, from accelerator release to brake contact; furthermore, the rectus femoris activity was delayed, and the simultaneous activity between the rectus femoris and biceps femoris was low. Furthermore, elderly drivers tended to have low hip adduction velocity and tended to switch pedals by hip internal rotation. Thus, the alteration in joint movements and muscle activity of elderly drivers can reduce their pedal operability and may be related to the occurrence of pedal errors.

Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.

Automatization and autonomous vehicles can drastically improve elderly drivers' safety and mobility, with lower costs to the driver and the environment. While autonomous vehicle technology is developing rapidly, much less attention and resources have been devoted to understanding the acceptance, attitudes, and preferences of vehicle automatization among driver groups, such as the elderly. In this study, 236 elderly drivers (≥65 years) evaluated four vehicles representing SAE levels 2–5 in terms of safety, trustworthiness, enjoyment, reliability, comfort, ease of use, and attractiveness, as well as reporting preferences for vehicles employing each of the four levels of automation. The results of a repeated-measures ANOVA showed that the elderly drivers rated the SAE level 2 vehicle highest and the fully automated vehicle (SAE 5) lowest across all attributes. The preference for the vehicle declined as a function of increasing automatization. The seven attributes formed an internally coherent “attitude to automatization” scale, a strong correlate of vehicle preference. Age or annual mileage were not related to attitudes or preferences for automated vehicles. The current study shows that elderly drivers' attitudes toward automatization should be studied further, and these results should be taken into account when developing automated vehicles. The full potential of automatization may not be realized if elderly drivers are ignored.