The influence of alcohol (0.5‰) on the control and manoeuvring level of driving behaviour,finding measures to assess driving impairment: A simulator study

ObjectiveThe influence of psychoactive substances on driving performance and traffic safety has been extensively studied. Research on the influence of alcohol at the control level of behaviour (i.e. automated processes) has been well established and has shown that the ability to operate a vehicle decreases with rising alcohol levels. However, results one level higher at the manoeuvring level (i.e. conscious processes), are inconsistent. The current study aimed to replicate findings on the influence of alcohol on the control level of behaviour and investigate effects on the manoeuvring level in order to find suitable measures to assess driving impairment.MethodThe study was double-blind, placebo-controlled with a counterbalanced treatment order and a two-way crossover design. Thirty participants performed tasks in a driving simulator under the influence of alcohol (0.5‰) and a placebo. In the driving tasks the control level of behaviour (swerving, average speed, and speed variation) was investigated, as well as the manoeuvring level of behaviour (distance to other traffic during an overtaking manoeuvre, reaction time to a traffic light turning amber, and response to a suddenly merging car).ResultsAs expected, alcohol affected the control level of behaviour negatively. Participants swerved more and showed more speed variation after alcohol intake. The manoeuvring level of driving behaviour was also affected by alcohol. The distance to other drivers during an overtaking manoeuvre was smaller under the influence of alcohol. Results on reaction time were however less straightforward. Reaction time increased significantly under the influence of alcohol when reacting to a traffic light but not in reaction to a car unexpectedly merging into traffic. When analysing behaviour in reaction to these different events in more detail it became clear that they were responded to in varying manners, making it difficult to find an average impairment measure.ConclusionsThe deteriorating effect of alcohol at the control level of driving behaviour was replicated, confirming the suitability of the standard deviation of lateral position and the variation in speed as measures of impairment. At the manoeuvring level, the kept distance to the leading car during an overtaking manoeuvre appeared to be a suitable measure to assess impairment as well as reaction time to a traffic light. The current study also confirms the difficulties in evaluating complex driving behaviour and the need for more research on this subject.     

“Don’t you know I own the road?” The link between narcissism and aggressive driving

Aggressive drivers can make driving dangerous. Over 50% of traffic fatalities are caused by aggressive driving. This research tests whether narcissists are more aggressive drivers than other individuals. Narcissists think they are special people who deserve special treatment. When they don’t get the special treatment they think they deserve, narcissists often lash out at others in an aggressive manner. Narcissists might think they “own the road” and can drive anyway they want, and that other drivers should get out of their way. In the article, we conduct three studies to test the link between narcissism and aggressive driving. In Studies 1 (N = 139) and 2 (N = 100), Luxembourgish motorists completed a measure of narcissism and a self-report measure of aggressive driving. In Study 3 (N = 60), American university students completed a measure of narcissism and then completed a driving simulation scenario that contained a number of frustrating elements. Several measures of aggressive driving and road rage were obtained. In all three studies, narcissism was positively related to aggressive driving. A meta-analysis found an average correlation of r = 0.35 across the three studies. This research replicates previous research linking narcissism to aggression, and extends it to a driving context.     

The tendency of drivers to pass other vehicles

The purpose of this study was to assess the speed differential threshold—if there is one—at which drivers decide to pass a lead vehicle. Drivers in a simulator encountered vehicles in front that were programmed to travel at speeds that were similar, slightly below, or even slightly above the drivers’ own speed. The study involved a total of 152 such passing opportunities. In almost all of the encounters with slower vehicles (traveling at speeds slower than 3 km/h of the driver) they passed them, and in two thirds of the encounters when the lead vehicles were moving at their speed they passed them too. Most surprising was that in 50% of the encounters drivers passed the lead vehicle when it was traveling faster than their average speed. In these situations drivers actually increased their own speed substantially to accomplish the passing maneuver, despite the fact that not passing the lead vehicle would not have caused any delays. The tendency to pass appears to be related to the drivers’ own speed variability: the more variable the driver’s speed the more likely he or she was to pass the vehicle ahead even when its speed was greater than their average speed. The results are interpreted in terms of (a) driver aggression, and (b) association of car following with added effort, attention overload, or risk. The latter explanation implies that the tendency to pass vehicles may be reduced with the introduction of in-vehicle technologies such as adaptive cruise control.     

Driving errors,estimated performance and individual characteristics under simulated and real road traffic conditions – A validation study

BackgroundThe suitability of driving simulators for the prediction of driving behaviour in road traffic has been able to be confirmed in respect of individual assessment parameters. However, there is a need for overarching approaches that take into account the interaction between various influencing factors in order to establish proof of validity. The aim of this study was to explore the validity of our driving simulator in respect of its ability to predict driving behaviour based on participants‘ observed driving errors and driver’s individual characteristics.Method41 healthy participants were assessed both in a Smart-Realo-Simulator and on the road. By means of linear modelling, the correlation between observed driving errors was investigated. In addition, the influence of self-reported and externally assessed driving behaviour as well as individual parameters (education and training; driving history) were analysed.ResultsBy including these factors, 58% of the variance could be explained. For observed driving errors, a relative validity was established. For self-reported and externally assessed driving behaviour, an absolute to relative validity emerged. The amount of time spent in education and training proved to have a significant influence on driving performance in the simulator, but not on the road.DiscussionIn general, our results confirmed the validity of our driving simulator with regard to observed and self-reported driving behaviour. It emerged that education and training as potential indicators of cognitive resources played a differential role regarding the study conditions. Since real road driving is considerably automated in experienced drivers, this result suggests that simulation-related behavioural regulation is challenged by additional cognitive demands as opposed to behavioural regulation extending to real road driving. However, the source of these additional cognitive demands remains currently elusive and may form the subject of future research.     

Tactile detection response task: Metrics for assessing drivers’ cognitive workload

The Tactile Detection Response Task (TDRT) has been used to assess the cognitive workload of driver distraction with response time and miss rate as metrics of cognitive workload. However, it is not clear which metric is more sensitive and whether sensitivity is maintained for visual tasks. The objective of this study was to assess the sensitivity of the TDRT to changes in cognitive workload and to examine whether the sensitivity depends on task modality. A driving simulator study was conducted with 24 participants. The study included restaurant selection tasks with three presentation modalities (auditory, visual, and hybrid) and two difficulty levels (low and high). The high difficulty level was designed to be more cognitively demanding than the low difficulty level. Mixed-effects models were applied to examine the TDRT metrics and task difficulty level. The model controlled for age group, gender, and included a random effect for participants. The high difficulty level of the auditory tasks significantly increased the likelihood of missing a TDRT stimulus. No statistically significant differences were observed for visual and hybrid tasks. TDRT response time was not significantly associated with the difficulty level, regardless of task modality. In this study, the binary outcome TDRT miss was thus considered a more sensitive metric of cognitive workload than TDRT response time. TDRT response time can still be used to measure cognitive workload when tasks are relatively easy and the TDRT miss rate is close to zero. In addition, the sensitivity of the TDRT miss diminished for tasks that involved a visual component. Researchers who use TDRT to measure the cognitive workload associated with visual tasks should be aware of this limitation.     

The role of motion platform on postural instability and head vibration exposure at driving simulators

This paper explains the effect of a motion platform for driving simulators on postural instability and head vibration exposure. The sensed head level-vehicle (visual cues) level longitudinal and lateral accelerations (a_x,sensed = a_{x_head} and a_y,sensed = a_{y_head}, a_yv = a_{y_veh} and a_yv = a_{y_veh}) were saved by using a motion tracking sensor and a simulation software respectively. Then, associated vibration dose values (VDVs) were computed at head level during the driving sessions. Furthermore, the postural instabilities of the participants were measured as longitudinal and lateral subject body centre of pressure (X_CP and Y_CP, respectively) displacements just after each driving session via a balance platform. The results revealed that the optic-head inertial level longitudinal accelerations indicated a negative non-significant correlation (r = −.203, p = .154 > .05) for the static case, whereas the optic-head inertial longitudinal accelerations depicted a so small negative non-significant correlation (r = −.066, p = .643 > .05) that can be negligible for the dynamic condition. The X_CP for the dynamic case indicated a significant higher value than the static situation (t(47), p < .0001). The VDV_x for the dynamic case yielded a significant higher value than the static situation (U(47), p < .0001). The optic-head inertial lateral accelerations resulted a negative significant correlation (r = −.376, p = .007 < .05) for the static platform, whereas the optic-head inertial lateral accelerations showed a positive significant correlation (r = .418, p = .002 < .05) at dynamic platform condition. The VDV_y for the static case indicated a significant higher value rather than the dynamic situation (U(47), p < .0001). The Y_CP for the static case yielded significantly higher than the dynamic situation (t(47), p = .001 < 0.05).     

Other road users’ adaptations to increase safety in response to older drivers’ behaviour

Changes in physical and cognitive abilities not only challenge the driving ability of older adults, in some situations age-related changes in driving behaviour require other road users to adapt their behaviour to maintain a safe traffic situation. In this study, we aimed to map age-related differences in driving behaviour and assess the impact on other road users. A group younger and a group older adults drove four different routes containing challenging situations (e.g., merging into motorway traffic) in a driving simulator while measures of driving behaviour were collected. Other road users’ deceleration responses to the driver’s behaviour were also collected as a measure of behavioural adaptation. Our results showed similar driving performance between young and older drivers when task complexity was low, but reduced performance in older drivers when tasks requirements increased. Lower driving speed and longer waiting times that were observed in older drivers can be interpreted as compensatory behaviour aimed at creating more time to lower task requirements. Crucially, in a non-time critical situation this compensatory behaviour was found to be successful, however in a time-critical situation (merging onto a motorway) this strategy had negative side effects because other road users had to decelerate in order to keep a safe distance. Our results show the importance of anticipation and adaptation by other road users for the success of older driver’s strategies and traffic safety.     

Comparative assessment of the behaviour of drivers with Mild Cognitive Impairment or Alzheimer’s disease in different road and traffic conditions

The objective of this research was the analysis of the driving performance of drivers with Mild Cognitive Impairment (MCI) or Alzheimer’s disease (AD), in different road and traffic conditions, on the basis of a driving simulator experiment. In this experiment, healthy “control” drivers, patients with MCI, and patients with AD, drove at several scenarios at the simulator, after a thorough neurological and neuropsychological assessment. The scenarios include driving in rural and urban areas in low and high traffic volumes. The driving performance of healthy and impaired drivers was analysed and compared by means of Repeated Measures General Linear Modelling techniques. A sample of 75 participants was analysed, out of which 23 were MCI patients and 14 were AD patients. Various driving performance measures were examined, including longitudinal and lateral control measures. The results suggest that the two examined cerebral diseases do affect driving performance, and there were common driving patterns for both cerebral diseases, as well as particular characteristics of specific pathologies. More specifically, cognitively impaired drivers drive at lower speeds and with larger headway compared to healthy drivers. Moreover, they appear to have difficulties in positioning the vehicle on the lane. The group of patients had difficulties in all road and traffic environments, and especially when traffic volume was high. Most importantly, both cerebral diseases appear to significantly impair reaction times at incidents. The results of this research suggest that compensatory behaviours developed by impaired drivers are not adequate to counterbalance the direct effects of these cerebral diseases on driving skills. They also demonstrate that driving impairments increase as cognitive impairments become more severe (from MCI to AD).