Driving is an important, autonomous daily activity, which underpins personal mobility in society [1
]. It is a vital skill that requires the use of multiple neuropsychological processes such as cognitive, visual, perceptual and data processing abilities [2
It has been well reported that older adults tend to experience cognitive decline with increased age [3
] and this decline can be accentuated by the neural brain damage caused by a stroke [7
], thereby having a subsequent adverse impact on their driving ability [9
]. Although studies show returning to driving post-stroke with rates between 30 and 68% reported in Australia and other countries [10
], since the post-stroke population is expected to rise to over 132,000 by 2050 in Australia [11
] and driving is still a key aspect of maintaining their independence, the population of post-stroke individuals who wish to return to driving is likely to rapidly increase [12
]. In consequence, ensuring a safe return to driving through accurate driving measurements is required, particularly as the older driver population and post-stroke drivers are at an increased risk of a crash and of fatal injuries due to increased frailty [13
Currently, driving simulators and on-road driving assessments are the two main methods used to assess driving behaviours, such as lane keeping performance [16
]. For the simulated driving scenarios, the participants perform a lane maintaining task and their lane deviation is calculated automatically by a driving simulator program [16
]. In contrast, on-road assessments involve completing a variety of driving tasks on-road, in a licensed vehicle, and these are considered the “gold standard” because they take place in the real world and are therefore more likely to gain a more accurate representation of drivers’ performance [18
]. Although preferable as an assessment method, it is hard to detect subtle variations between drivers in an on-road assessment if they do not make any errors as traditional driving assessments rely on subjective observations from driving evaluators. A between-groups study included a post-stroke driver group and a group of similarly aged older control drivers who were observed for their driving behaviours in simulator-based driving scenarios [20
], and no differences were found in the amount of the perceived tasks demand required to complete the driving tasks.
Anstey and Wood [2
] pointed out that using a fixed driving route for on-road testing is an efficient strategy to examine the participant’s driving performance. Therefore, the optimal approach is to record their driving in naturalistic settings at a microscopic level using a fixed driving route. An advanced global navigation satellite system, such as GPS technology, can be applied to tracking vehicle movements and trajectories, which can be used to ascertain the driver’s driving behaviour on-road assessment.
The driving trajectory and behaviours are both dependent on the position data from the Global Positioning System (GPS), and thus the accuracy and precision of the GPS data become the key points in analysing the driver’s driving trajectory and behaviours. Recently, multiple satellite systems have contributed significantly to global navigation and positioning systems with regard to precision and availability. Generally, the combination of multi-global navigation satellite system (GNSS) receivers can be used to collect and detect a driver’s driving trajectory [21
]. It can record the position data of a vehicle to one tenth of a second with a high degree of precision (1 decimetre), which is important for improving driving trajectory detection accuracy. In comparison with single GPS technology, multi-GNSS can provide a better approach to recording more accurate position information since more satellites can be tracked, which will be efficient in tracking the driver’s driving trajectory. To improve the raw position data, corrections can be applied to the recorded position data when the accuracy is particularly important [22
]. Specifically, efficient GPS position techniques can be used to correct errors in the position data. For example, real-time kinematic (RTK) can be applied to enhance the accuracy and precision of the position data which can be fitted to track driving trajectories. The RTK technique introduces not only GNSS code pseudo-range measurements to compute its position, and atmospheric errors such as troposphere errors and ionosphere errors could be considered and evaluated [23
], it also applies carrier phase measurements, which can provide positions that are orders of magnitude. Therefore, the position data could reach an accuracy level of between a millimetre and a centimetre [22
]. By adopting the millimetre to a centimetre measure of the driving trajectory using multi-GNSS RTK technologies, lane keeping performance can be assessed at a high accuracy level in a geographic information system (GIS) platform [22
The primary aim of this study is to explore the quantitative variations in vehicle control performance between post-stroke and normal older drivers using multi-GNSS and RTK techniques. Based on the solid literature review [17
], the objectives of this paper are to examine the vehicle control performance in post-stroke driving in comparison with normal drivers’ performance in older drivers through a pilot case–control study and to explore potential indicators of post-stroke driving that can be employed in driving intervention design for this cohort of drivers.
The underlying motivation of this pilot study was to assess the feasibility, appropriateness and effectiveness of using advanced GPS modelling as a tool to differentiate the driving performance of post-stroke drivers from that of normal older drivers. A subtle difference in driving competency is hard to detect using observation, but GNSS and RTK technologies enable accuracy of vehicle movement tracking and therefore provide detailed information on driving performance. The GNSS receiver can obtain the signal from multiple satellite systems. Additionally, RTK technology was applied after loading the trajectory data from GNSS receiver, which uses a known position of a base station to correct the recorded trajectory data and improves the data accuracy and precision to a high level of data accuracy (sub-decimetre level). Thus, the subtle difference in the participants’ driving trajectory and behaviour was explored, which indicated that the GNSS modelling was an appropriate and effective tool to explore on-road driving performance
Although multi-GNSS RTK for tracking driving behaviour has previously been utilised [22
], as far as the authors are aware, this is the first study to investigate post-stroke driver performance on-road. The present study found that post-stroke adults performed more consistently in the straight line section and less consistently than the normal drivers in the exit to the roundabout. However, with the exception of the exit on the roundabout, there were no statistically significant differences in the speed maintenance or lane deviation performance between the post-stroke drivers and the normal adults. This would suggest that despite the differences in lane and speed deviation between groups, the post-stroke drivers were just as safe as the control participants. This aligns well with the fact that all the post-stroke adults had been cleared to drive by a medical professional.
A small sample size is one important limitation of this study, which increases the risk of a type II error; however, as the α value was set to 0.05, only statistical differences at the exit on the roundabout between two groups were found in the study. Further research with a greater sample size is required to address this.
The gender imbalance (3 females, 11 males) in the post-stroke group is another limitation that may affect the comparison of the post-stroke and normal groups, particularly as male post-stroke subjects are more likely to return to driving than females [36
]. However, as volunteer sampling was employed, it was difficult to recruit enough participants to gender match. Further research with a greater sample and with gender matching would address this problem.
For the on-road driving test, each participant performed the driving test using his/her own vehicle. It is possible that this may have influenced their lane keeping and driving trajectory due to the tracking quality and vehicle transmission; however, it can be argued that participants’ performance was likely to be most valid in a vehicle with which they were familiar. Further, as some participants required vehicle alterations (e.g., a spinner knob for hemiplegia or hemiparesis) for safety and insurance reasons, it was decided that all participants were to use to their own vehicle.
Although the same fixed route was applied for each participant in order to minimise the effects of road changes, one of the major difficulties in on-road testing is that the road conditions are likely to change between assessments, e.g., traffic or weather. In order to control for this as much as possible, participants were assessed between the hours of 10.00 a.m. and 4.00 p.m. to avoid peak hour congestion and assessors noted whether there was any significant change in the driving route, e.g., due to bad weather, road works or traffic accidents.
It can be concluded that lane keeping might be an indicator of driving performance when driving at a U-turn, particularly exiting the roundabout (complex driving task), while speed control might be superior in revealing driving performance in a straight line. Cognitively demanding driving situations, such as U-turns and particularly exiting the roundabout, create a challenge for post-stroke older drivers. This may be ascribed to the higher levels of cognitive abilities required to maintain the lane along the changing geometry of the road [17
]. For understanding the association between lane keeping, speed maintenance and cognitive abilities, especially divided and selective attention requires further research that might explain why some post-stroke drivers’ mean SDLD stays relatively high.
The findings of this study demonstrate the appropriateness, feasibility and effectiveness of assessing driving behaviours of post-stroke older drivers. It is strongly recommended that SDLD calculated from an accurate vehicle movement trajectory is a sensitive and effective measure for driving assessment in this cohort population. Future work will need to examine and model older drivers’ lane keeping and speed regulation in the face of hazardous driving situations. Further educational and training programs based on the findings of this study could be developed to enhance post-stroke older drivers’ behaviour behind the wheel; for example, neuropsychological training to improve post-stroke older drivers’ executive function [37
]; and driving intervention training to improve lane keeping performance [38
] at challenging driving sections.