Associations between motorized transport access, out-of-home activities, and life-space mobility in older adults in Japan.

BACKGROUND: Motorized transport access and out-of-home activities are two potential correlates of Life-space mobility (LSM), a common research topic in mobility studies of older adults. These correlates remain mostly unexplored in previous literature and relating them with LSM can reveal directions for improving the LSM of older adults. METHODS: The associations between motorized transport access, out-of-home activities, and LSM were examined using data from 1,333 older adults (mean age = 70.63) living in 15 cities and towns in Japan. LSM was assessed using composite life-space assessment (LSA) scores. Motorized transport access was measured using dummies showing whether a person had car access (divided into five levels) and used public transport (bus and railway), and out-of-home activities were measured using the number of various activities that were conducted during the most recent weekday and weekend day. Generalized linear models were used to assess the associations. RESULTS: The sample was dominated by males (74.42%), with more than half of the sample had their own cars. On average, each respondent had four activities during two survey days, and shopping was the most common activity. The results showed that owning a car and using railway, as well as various activities were associated with increased composite LSA scores, whereas no cars or only shared cars in home were associated with decreased composite LSA scores. However, these associations differed between males and females. CONCLUSIONS: In this study, different levels of motorized transport access and different types of out-of-home activities were found to associate differently with composite LSA scores. Based on these findings, we suggest that policymakers should provide more transport access, pay more attention to the LSM of older adults with high clinics/hospital activities, and trigger more shopping and daily leisure activities for older adults to improve the LSM of this population.

Effects of one-pedal automobile operation on the driver's emotional state and cognitive workload.

A one-pedal system for operating an electric vehicle allows drivers to flexibly accelerate and decelerate (and even stop) by using just an accelerator pedal. Based on previous findings, one-pedal operation is considered to have the potential to increase positive emotions and decrease cognitive workload. To test this possibility, the present study compared the emotional state and cognitive workload between one-pedal and conventional two-pedal operation. Participants drove a vehicle on public roads, and driving enjoyment (i.e., pleasure and immersion) and the cognitive workload (i.e., ease and effortlessness) were assessed by means of questionnaires. In addition, physiological variations associated with driving pleasure and difficulty were assessed by electroencephalography (EEG). Both the questionnaire and EEG results revealed an increase in driving enjoyment in one-pedal operation. On the other hand, only the EEG results suggested a decrease in the cognitive workload in one-pedal operation; the questionnaire results did not show a significant difference between the pedal conditions. These findings support the notion that one-pedal operation has a positive influence on the driver's mental state, though its influence on the cognitive workload will require further investigation. We discuss future directions toward a better understanding of the effects of one-pedal operation on the driver's mental state.

Effects of cognitive and visual loads on driving performance after take-over request (TOR) in automated driving.

The present study investigated effects of cognitive and visual loads on driving performance after take-over request (TOR) in an automated driving task. Participants completed automated driving in a driving simulator without a non-driving related task, with an easy non-driving related task, and with a difficult non-driving related task. The primary task was to monitor the environment and the system state. An N-back task and a Surrogate Reference Task (SuRT) were adapted to induce cognitive and visual loads respectively. The system followed a front vehicle automatically. Driving performance was measured by responses to a critical event (appearance of a broken-down car) after the automated system issued TOR and then terminated. High subjective difficulty of the N-back task was related to increased time and increased steering angle variance in the time course from onset of steering control to lane change, while high subjective difficulty of SuRT was related to increased steering angle variance in the time course after lane change. This suggests that both cognitive and visual loads affect driving performance after TOR in automated driving, but the effects appear in different time courses.

Age-related differences in effects of non-driving related tasks on takeover performance in automated driving.

INTRODUCTION: During SAE level 3 automated driving, the driver's role changes from active driver to fallback-ready driver. Drowsiness is one of the factors that may degrade driver's takeover performance. This study aimed to investigate effects of non-driving related tasks (NDRTs) to counter driver's drowsiness with a Level 3 system activated and to improve successive takeover performance in a critical situation. A special focus was placed on age-related differences in the effects. METHOD: Participants of three age groups (younger, middle-aged, older) drove the Level 3 system implemented in a high-fidelity motion-based driving simulator for about 30 min under three experiment conditions: without NDRT, while watching a video clip, and while switching between watching a video clip and playing a game. The Karolinska Sleepiness Scale and eyeblink duration measured driver drowsiness. At the end of the drive, the drivers had to take over control of the vehicle and manually change the lane to avoid a collision. Reaction time and steering angle variability were measured to evaluate the two aspects of driving performance. RESULTS: For younger drivers, both single and multiple NDRT engagements countered the development of driver drowsiness during automated driving, and their takeover performance was equivalent to or better than their performance without NDRT engagement. For older drivers, NDRT engagement did not affect the development of drowsiness but degraded takeover performance especially under the multiple NDRT engagement condition. The results for middle-aged drivers fell at an intermediate level between those for younger and older drivers. Practical Applications: The present findings do not support general recommendations of NDRT engagement to counter drowsiness during automated driving. This study is especially relevant to the automotive industry's search for options that will ensure the safest interfaces between human drivers and automation systems.

Effects of scheduled manual driving on drowsiness and response to take over request: A simulator study towards understanding drivers in automated driving.

Because current automated vehicles have operational limitations, it is important to ensure that the fallback-ready driver is able to perform appropriately when required to take over control of the vehicle. However, time-related increase in driver drowsiness is well-known, and drowsy driving can affect response to take-over request (TOR). It was previously reported that a scheduled period of manual driving during automated driving was beneficial in maintaining driver arousal level. The present driving simulator study investigates the effects of scheduled manual driving on driver drowsiness and performance, as well as age differences therein. A total of 115 participants, whose gender was balanced and age was distributed uniformly from 20 to 70 years, drove an automated vehicle for 31 min, and a TOR was prompted before a collision event. A between-subjects design comprised two conditions: with versus without a scheduled 10-min interval of manual driving that ended 10 min before TOR. The Karolinska Sleepiness Scale and eyeblink durations estimated from electrooculograms (EOG) were used to subjectively and objectively measure participant's drowsiness. Reaction time, standard deviation of steering wheel angle, and minimum Time-to-Collison (TTC) were extracted to measure driver performance in response to TOR. The alleviating effect on drowsiness of 10-min scheduled manual driving became non-significant after another 10-min period of automated driving. Although the scheduled manual driving had no significant effect for younger drivers, older drivers reacted significantly more slowly in both steering and braking at the critical event. These findings provide essential insights for human-vehicle interactions: Scheduled manual driving cannot maintain drivers' arousal level for 10 min afterwards, and for older drivers, it would be better to avoid unnecessary task-switching between manual and automated driving.