The effect of a secondary task on drivers' gap acceptance and situational awareness at junctions.

The current studies explored the roles of the visuospatial and phonological working memory subsystems on drivers' gap acceptance and memory for approaching vehicles at junctions. Drivers' behaviour was measured in a high-fidelity driving simulator when at a junction, with, and without a visuospatial or phonological load. When asked to judge when to advance across the junction, gap acceptance thresholds, memory for vehicles and eye movements were not different when there was a secondary task compared to control. However, drivers' secondary task performance was more impaired in the visuospatial than phonological domain. These findings suggest that drivers were able to accept impairment in the secondary task while maintaining appropriate safety margins and situational awareness. These findings can inform the development of in-car technologies, improving the safety of road users at junctions. Practitioner summary: Despite research indicating that concurrent performance on working memory tasks impairs driving, a matched visuospatial or phonological memory load did not change drivers' gap acceptance or situational awareness at junctions. Drivers displayed appropriate compensatory behaviour by prioritising the driving task over the visuospatial secondary task. Abbreviations: ROW: right of way; RIG: random time interval generation.

Comparing drivers' visual attention at Junctions in Real and Simulated Environments.

Driving simulation is widely used to answer important applied research questions, however, it is vital for specific driving tasks to undergo appropriate behavioural validation testing. Many previous validation studies have used simple driving tasks and measured relatively low-level vehicle control. The purpose of the current study was to investigate whether drivers' visual attention at intersections with different levels of demand, are similar in the simulator and on the road. Unlike simpler driving tasks, crossing intersections requires complex interactions with other vehicles governed by sequences of head and eye movements that may not be accurately captured in a simulated environment. In the current study we directly compare performance at simulated junctions with the same participants' behaviour in a real car. We compared drivers' visual attention in a high-fidelity driving simulator (instrumented car, 360-degree screen) and on-road in both low and medium demand driving situations. The low and medium demand driving situations involved the same motor movements, containing straight on, right turn and left turn manoeuvres. The low demand situations were controlled by the road environment and traffic lights, whereas medium demand situations required the driver to scan the environment and decide when it was safe to pull out into the junction. Natural junctions in Nottingham were used for the on-road phase and the same junctions were recreated in the simulator with traffic levels matched to those that were encountered on the real roads. The frequency and size of drivers' head movements were not significantly different between manoeuvres performed in the simulator and those conducted when driving on real roads. This suggests that drivers' broad search strategies in the simulator are representative of real-world driving. These strategies did change as a function of task demand - compared to low demand situations, behaviour at the medium demand junctions was characterised by longer junction crossing times, more head movements, shorter fixation durations and larger saccadic amplitudes. Although patterns of head movements were equivalent on road and in the simulator, there were differences in more fine-grained measures of eye-movements. Mean fixation durations were longer in the simulator compared to on-road, particularly in low-demand situations. We interpret this as evidence for lower levels of visual engagement with the simulated environment compared to the real world, at least when the task demands are low. These results have important implications for driving research. They suggest that high fidelity driving simulators can be useful tools for investigating drivers' visual attention at junctions, particularly when the driving task is of at least moderate demand.

The 'Saw but Forgot' error: A role for short-term memory failures in understanding junction crashes?

Motorcyclists are involved in an exceptionally high number of crashes for the distance they travel, with one of the most common incidents being where another road user pulls out into the path of an oncoming motorcycle frequently resulting in a fatal collision. These instances have previously been interpreted as failures of visual attention, sometimes termed 'Look but Fail to See' (LBFTS) crashes, and interventions have focused on improving drivers' visual scanning and motorcycles' visibility. Here we show from a series of three experiments in a high-fidelity driving simulator, that when drivers' visual attention towards and memory for approaching vehicles is experimentally tested, drivers fail to report approaching motorcycles on between 13% and 18% of occasions. This happens even when the driver is pulling out into a safety-critical gap in front of the motorcycle, and often happens despite the driver having directly fixated on the oncoming vehicle. These failures in reporting a critical vehicle were not associated with how long the driver looked at the vehicle for, but were associated with drivers' subsequent visual search and the time that elapsed between fixating on the oncoming vehicle and pulling out of the junction. Here, we raise the possibility that interference in short-term memory might prevent drivers holding important visual information during these complex manoeuvres. This explanation suggests that some junction crashes on real roads that have been attributed to LBFTS errors may have been misclassified and might instead be the result of 'Saw but Forgot' (SBF) errors. We provide a framework for understanding the role of short-term memory in such situations, the Perceive Retain Choose (PRC) model, as well as novel predictions and proposals for practical interventions that may prevent this type of crash in the future.

Comparing car drivers' and motorcyclists' opinions about junction crashes.

Motorcyclists are involved in a disproportionate number of crashes given the distance they travel, with a high proportion of these crashes occurring at junctions. Despite car drivers being solely responsible for many road crashes involving a motorcycle, previous research has mostly focussed on understanding motorcyclists' attitudes towards their own safety. We compared car drivers' (n = 102) and motorcyclists' (n = 579) opinions about junction crashes using a web-based questionnaire. Motorcyclists and car drivers were recruited in similar ways so that responses could be directly compared, accessing respondents through driver/rider forums and on social media. Car drivers' and motorcyclists' opinions were compared in relation to who they believe to be blameworthy in situations which varied in specificity, ranging from what road user they believe is most likely to cause a motorcyclist to have a road crash, to what road user is at fault in four specific scenarios involving a car and motorcycle at a junction. Two of these scenarios represented typical 'Right of way' (ROW) crashes with a motorcycle approaching from the left and right, and two scenarios involved a motorcycle overtaking another vehicle at the junction, known as 'Motorcycle Manoeuvrability Accidents' (MMA). Qualitative responses were analysed using LIWC software to detect objective differences in car drivers' and motorcyclists' language. Car drivers' and motorcyclists' opinions about the blameworthiness of accidents changed depending on how specific the situation was that was being presented. When respondents were asked about the cause of motorcycle crashes in a general abstract sense, car drivers' and motorcyclists' responses significantly differed, with motorcyclists more likely to blame car drivers, demonstrating an in-group bias. However, this in-group favouritism was reduced when asked about specific scenarios, especially in MMA situations which involve motorcyclists manoeuvring their motorcycles around cars at a junction. In the four specific scenarios, car drivers were more likely to blame the car driver, and motorcyclists were more likely to blame the motorcyclist. In the typical ROW scenarios, the responses given by both road users, as analysed by the LIWC, show that the law is taken into account, as well as a large emphasis on the lack of observation given around junctions, especially from car drivers. It is concluded that the perception of blameworthiness in crashes is very much dependent on the details of the crash, with a more specific situation eliciting a fairer evaluation by both car drivers and motorcyclists.