Australia’s ageing population has brought the issue of older drivers into a sharper focus. By 2030, about one in four drivers will be above the age of 65. On the other hand, the traffic situation has become ever complicated. Not all older drivers are unsafe, and the statistics don’t reflect individual’s driving ability. While there is a strong emphasis around the world for older adults to maintain their mobility for as long as possible, the challenge is to develop appropriate evaluation methods to identify those older drivers at higher risk of road crashes and to provide intervention as early as possible.
Age alone is not significantly correlated with older drivers’ lane maintenance performance
An interdisciplinary study in older drivers’ driving behavior, using a psycho-geoinformatics approach, indicates that age alone is not significantly correlated with older drivers’ lane maintenance performance. Rather, older drivers’ cognitive abilities, in particular visual attention and executive function, are good predictors for their vehicle control capability behind the wheel.
A better understanding of older drivers’ behavior on the road
This study examined older drivers’ mobility and behavior through comprehensive measurements of driver-vehicle-environment interaction and investigated the associations between driving behavior and cognitive functions. Data were collected and analysed for 50 older drivers using eye tracking, global navigation satellite system (GNSS) tracking, and a geographic information system (GIS). Results showed that poor selective attention, spatial ability and executive function in older drivers adversely affect lane keeping, visual search and coordination. Visual-motor coordination measure is sensitive and effective for driving assessment in older drivers.
Visual-motor coordination measure is effective for driving assessment in older drivers
We developed models, such as the visual-motor coordination model, which tracks how eye movements change and move in relation to vehicle motor movements. The idea was to understand how motor movements may relate to drivers’ cognitive perception, including how quickly perception can lead to driving actions. Eye movements can be geocoded, while a GNSS can be used to obtain precise car movements to understand each minor change in a car’s positioning as eye movement change occurs. In fact, for older drivers, differences between car and eye movements have been more noticeable, where research has shown right turn at intersections and exit of roundabouts give older drivers greater difficulties between perception and ability to react in manoeuvring cars effectively.
Visual-coordination abilities under investigation
The psycho-geoinformatics approach provided a more detailed image of older drivers than many other studies. Particularly the measurement of visual-motor coordination is new in driving safety research. The impact of specific cognitive abilities on driving behavior shows that effects are most noticeable in visual-spatial abilities and executive function condition in this group of older drivers.
Driving scenarios or tasks have a significant influence on older drivers’ visual patterns, lane keeping and coordination performance. Poorer visual-spatial and visual-motor abilities appear to be particularly associated with low-performing driving behaviors. It is strongly recommended that visual-motor coordination is a sensitive and effective measure for driving assessment in the older population. Individual older drivers must be aware of their abilities and limitations so that they can compensate by employing defensive lane keeping behaviors to enhance their driving competencies.
What are the implications of this approach for autonomous vehicle driving?
With the advent of automation of the driving task, the topic area of cognitive attention is in fact becoming even more important. When the vehicle is operating under its own control, it is arguably safe for the human in the vehicle to shift attention away from driving and the traffic situation. But some automation designs still require supervisory control by the human and readiness to take over on short notice. Therefore, some level of attention to the external road and traffic scene, and effective visual-motor coordination are still needed and critical for older people.
To make an autonomous vehicle more cognitive and artificially intelligent, it needs a system that uses certain advanced cognition theories, such as situation awareness theory and visual-motor coordination theory that we have described in our paper. We believe that one of the challenges of autonomous vehicles will be to imitate human behavior in driving interactions. This probably will create a long-term application for psycho-geoinformatics approach in driving research; for example, a psycho-spatial-temporal algorithm may be developed and integrated into the driverless car system architecture.