In 2021, the population aged 60 years and older was over 1 billion worldwide, and one in six people will be 60 years or older by 20301. In ageing societies, the proportion of elderly drivers are rapidly increasing. In Japan, one of the countries with the highest proportion of elderly citizens, 29.1% of the population was over 65 years old in 20212, which will be 37.7% by 20503. Moreover, the proportion of people over 70 years old with a driver’s license reached 14.5% in 2019, increasing by approximately 15 times during the past 30 years4.
Driving is an important way for the elderly to maintain their mobility, which is known to strongly affect their independence and quality of life5,6. However, elderly drivers are more likely to be involved in crashes at intersections, lane changing, and driving accidents involving multiple vehicles7–9. Some elderly people even have to give up driving due to the decline in their physical and mental conditions10–12.
The development of highly automated vehicles (HAV), also known as level 3 automation13, is expected to benefit elderly drivers by enhancing their mobility14–16. During level 3 automated driving, drivers can engage in non-driving related tasks (NDRTs) and can regain control of the vehicle when the system prompts a takeover request (TOR). Elderly drivers tend to engage in various NDRTs during simulated automated driving17, including entertainment, working, and dietary activities18.
1.1 Effects of NDRTs on takeover performance
Engaging in NDRTs is known to affect takeover performance, such as more collisions19 and more deviation from the center of the lane20. The negative effect of NDRT is especially crucial for elderly drivers, considering the decline in their driving ability. Therefore, elderly drivers’ behaviors after a TOR could be more cautious or conservative than younger drivers. For example, they prefer to brake more and maintain a longer safe distance21,22,17. Moreover, slower reactions and decision making among elderly drivers were found when regaining control after a TOR23. Moreover, after a period of manual driving, an increase in reaction time (RT) to a TOR was found for elderly drivers, but not for younger drivers24. Furthermore, Wu et al.25 revealed age-related differences in the interaction between NDRT and drowsiness and found that performing NDRT did not alleviate the drowsiness of elderly drivers but deteriorated their takeover performance. The distribution of visual attention can also differ between young and elderly drivers. That is, elderly drivers focused more on the road than NDRT compared with younger drivers26. Moreover, they checked their back mirrors less frequently and their fixation time became shorter when engaging in NDRT27.
Other studies reported no significant age effects on driving performance after a TOR22 or takeover time28. Despite the different study designs, these contradictory results suggest that other factors may underlie age-related differences. Li et al.29 investigated takeover performance, behavior, and perceptions of elderly people in different age subgroups, suggesting that elderly people should be considered as a heterogeneous group. Beyond chronological age, it is important to explore individual differences in driving performance in response to TOR using explicit factors.
1.2 Executive function and elderly drivers’ driving behaviors
Cognitive abilities decline with age30; therefore, elderly drivers with worse cognitive abilities are more likely to exhibit dangerous driving behaviors and be involved in crashes31–33. Driving is a complex task that requires various cognitive abilities. For instance, visual attention and visuospatial cognition are related to overall driving performance34,35: perceptual-cognitive capacity is related to speed control and crash risk36, simple RT is related to hazard perception37, and working memory is related to decision making38.
Adrian et al.39 concluded that functional abilities were more determinant than chronological age in predicting elderly drivers’ performance. Studies have shown that age-related decline in various cognitive tasks could be attributed to the aging of the frontal lobe, also known as the frontal aging hypothesis40,41. Furthermore, the frontal lobe has been demonstrated to be correlated with executive function (EF)42,43. EF comprises a set of higher-order cognitive processes that coordinate lower-level processes44, and can be defined as processes that control and regulate thought and action45. EF may play a crucial role in driving tasks because it requires complex cognition-required task sets to summarize information and supervise actions46, such as maintaining continued attention, dealing with irrelevant information, and adapting to different task requirements47.
Three main components of EF were distinguished: “Shifting,” “Updating,” and “Inhibition”48. The shifting component refers to shifting between multiple tasks, operations, and mental sets49. The updating component involves dealing with incoming information to replace the old, already irrelevant information50. The inhibition component can be described as an active or willed suppression of the tendency to react to more dominant or automatic responses48,51.
Previous research has revealed an important relationship between driving performance and EF. Daigneault et al. found that elderly drivers with lower executive abilities were more likely to be involved in traffic accidents46. More specifically, correlations between the three executive components and driving performance have been demonstrated in previous studies. To illustrate, shifting ability (mostly measured by trail making tasks) was found to be correlated with car crash risk31, road test driving behaviors34, hazard perceptions37, and driving errors in lane position52. Researchers also found that inhibition ability, which is related to selective attention, was associated with road test scores53, on-road driving performance35, and observation errors52. Updating ability, which is related to working memory54, plays an important role in drivers’ decision making38.
In comprehensive studies dedicated to the three main components of EF, updating39,55, inhibition56, and shifting39 components were found to be significantly correlated with driving performance. Walshe et al.47 reviewed various subprocesses of EF and concluded that inhibition and updating played important roles in car crashes. Moreover, EF components resulting from principal component analysis (PCA)55 and confirmatory factor analysis56 showed clear correlations between these latent factors and driving performance. The use of underlying, structured components yielded from the results of cognitive tests rather than pure task performance can alleviate the complexity of EF research brought on by the so-called “task impurity problem.” Executive tasks often involve other cognitive tasks; therefore, EF test results may provide additional individual differences unrelated to EF48. Exploring the correlation between driving performance and EF components as latent variables can avoid these uncertainties and produce more valid results56. Therefore, we expected that the use of latent components generated from EF task performance could effectively establish a correlation with takeover performance.
Overall, several studies have focused on age-related differences in takeover performance, as well as the importance of abilities related to EF in driving behaviors. However, research on the relationship between age-related differences in EF components and takeover performance in automated driving is rare. To fill this gap, we designed a series of computerized cognitive tests and simulated driving tasks to evaluate drivers’ EF and takeover performance. EF components were extracted by PCA and their correlations with takeover performance were investigated. The purpose of this exploratory study is two-fold: first, to examine whether age-related differences exist in EF and takeover performance in automated driving, and second, to explore the relationship between specific EF components and takeover performance.