The relationship between simulator sickness and driving performance in a high-fidelity simulator

Driving simulators are highly valuable tools for various applications such as research, training, and rehabilitation. However, they are also known to cause simulator sickness, a special form of traditional motion sickness. Common side effects of simulator sickness include nausea, headache, dizziness, eye-strain, and/or disorientation, all symptoms which may negatively impact driving performance. The goal of the present study was to investigate the relationship between simulator sickness and driving performance obtained in a high-fidelity driving simulator. Twenty-one healthy participants were engaged in a simulated driving task containing rural, city, and highway sections for approx. 25 min. Participants were asked to drive naturally while obeying traffic rules and completing common driving maneuvers (including reactions to sudden events). Driving performance was evaluated based on various driving measures, such as lane positioning, speed measures, following distance, or the number of steering reversals. Simulator sickness was measured before, during, and after the simulated drive using a combination of the Simulator Sickness Questionnaire and the Fast Motion Sickness scale. Overall, correlations between the level of simulator sickness and driving performance measures were low to moderate (r’s from -0.37 to 0.40) and were not significant. Additionally, participants who reported higher levels of simulator sickness did not differ with regards to their driving performance from those who reported lower simulator sickness scores. Our results suggest that the presence of simulator sickness is not strongly related to performance in a driving simulator.     

A validation study of driving errors using a driving simulator

BackgroundDriving simulators have become an important research tool in road safety. They provide a safer environment to test driving performance and have the capacity to manipulate and control situations that are not possible on-road.AimTo validate a laboratory-based driving simulator in measuring on-road driving performance by type and mean driving errors.MethodsParticipants were instructed to drive a selected route on-road. The same route was programmed in the driving simulator using the UC/Win-road software. All participants completed a background questionnaire. On-road driving behaviours of participants and driving behaviours in the simulator were assessed by an occupational therapist and two trained researchers using an assessment form. Interclass correlations were calculated to assess the inter-rater agreement between the researchers on driving behaviours. Paired t-tests were used to assess differences in driving performance between the simulator and on-road assessments.ResultsA convenience sample of 47 drivers aged 18–69 years who held a current Western Australian class C licence (passenger vehicle) were recruited into the study. The mean age was 34.80 years (SD: 13.21) with twenty-six males (55.32%) and 21 females (44.68%) completing the study. There was no statistical difference between the on-road assessment and the driving simulator for mirror checking, left, right and forward observations, speed at intersections, maintaining speed, obeying traffic lights and stop signs.ConclusionThe preliminary results provide early support for the relative validity of the driving simulator which may be used for a variety of road safety outcomes with reduced risk of harm to participants.     

Diverging diamond interchanges: A driving simulator study

This study assessed driver performance while navigating a Diverging Diamond Interchange (DDI) compared to a standard intersection in a driving simulator. A total of 201 Western Australian (WA) drivers aged 18–80 years completed the simulator drive and questionnaire. Measures of driving simulator performance assessed included time spent out of lane, number of lane excursions, compliance to the speed limit, crashes and near misses. Other driving measures, which were recorded by the researcher, included driver errors/violations such as red-light violations, wrong way violations and navigation errors. Qualitative information was also obtained in a post exit interview with each participant regarding the difficulties they experienced when driving through the DDI. A repeated-measure analysis of variance (r-ANOVA) was undertaken to examine differences in intersection type (DDI versus standard intersection) and driving performance measures from the driving simulator. The only significant result was compliance to the speed limit (F (1, 656) = 160.11, p < 0.001) on the driving simulator. A higher proportion of red-light violations were observed by the researcher as participants navigated through the DDI, compared to the standard intersection. Qualitative comments from participants also highlighted the need for better signage and road markings. Recommendations when DDIs are implemented include community education on speed limit compliance, avoidance of red-light violations and design improvements regarding signage and road markings.     

An evaluation of the effectiveness of countermeasures for improving the safety of dilemma zones: A driving simulator study

The main objective of this driving simulator study is to analyze the behavior of the driver at the start of the yellow signal of a signalized rural intersection and identify the most effective countermeasures for tackling the dilemma zone, namely an area on the intersection approach where vehicles at the start of the yellow phase can neither safely stop before the stop line nor cross the intersection. The following countermeasures were tested in the study on a sample of 48 drivers: green signal countdown timers, GSCT (C₁); a new pattern of vertical and horizontal warning signs (C₂); and an advanced on-board driver assistance system based on augmented reality (AR) and connected vehicle technologies (C₃). These countermeasures were tested and compared to a baseline condition (B) where no countermeasures were applied. Based on the results of this study, the C₂ and C₃ countermeasures have proven to be valid tools for reducing driver indecision when approaching signalized intersections at the start of the yellow signal. In fact, using C₂ and C₃, the length of the dilemma zone was equal to 30 m and 36 m, respectively, with a reduction of about 50%, as compared to the baseline condition (B). Moreover, a reduced number of false behaviors was recorded, as well as a greater consistency in driver decision-making behaviors. Conversely, the C₁ countermeasure did not lead to a significant improvement in the dilemma zone: an unnecessary increase in early stop rates was recorded, resulting in reduced intersection efficiency and operations.     

Car following with an inertia-oriented driving technique: A driving simulator experiment

BackgroundFor many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course.     

An analysis of factors affecting wrong-way driving at a highway entrance using a driving simulator among older drivers with cognitive decline

About 200 highway wrong-way driving cases occur annually in Japan, of which about 70% are caused by older drivers. The number of these cases has remained consistent despite adoption of full-fledged measures against wrong-way driving, thus demonstrating the need for further measures. Reports indicate that older drivers include those with cognitive decline. Moreover, it has been revealed that drivers over 75 years of age who caused wrong-way driving had lower test scores in the usual cognitive function tests performed at the time of licence renewal. However, there is no clear evidence on the relationship between cognitive decline and driving behaviour that stimulates wrong-way driving. In this paper, we reproduced a wrong-way driving approaching a right turn to an expressway exit in a virtual environment using a driving simulator for older drivers, including those with mild cognitive impairment (MCI). In addition to the measurement of driving behaviour and eye-tracking during simulator driving, we conducted a cognitive function test to investigate the relationship between driving behaviour and visual behaviour factors that initiate reverse running and cognitive decline. The results revealed that the reason for the start of a highway wrong-way driving may be a human error at the recognition stage of overlooking a visual wrong-way driving countermeasure, or a human error at the judgement stage of failing to suppress action despite noticing the visual countermeasure. Additionally, attentional and executive functions were related to the wrong-way initiative, although MCI did not affect it. This suggests that those who could not control their wrong-way driving behaviour despite observing the signs had specific executive function deficits.     

A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

Driver's avoidance characteristics to hazardous situations: A driving simulator study

When analyzing the causes of an accident, it is critical to determine whether the driver could have prevented the accident. In previous studies on the reaction times of drivers, the definition and values of reaction times vary, so applying reaction time is difficult. In such analysis, the driver’s reaction time from perception is required to determine whether the driver could have prevented the accident, but past studies are difficult to utilize in accident analysis as reaction time measurements were taken after the occurrence of hazardous situations. In this study, 93 subjects from age groups ranging from 20 s to 40 s participated in an experiment inside a full-scale driving simulator, to determine reaction time values that can be practically applied to accident analysis. A total of 4 hazardous accident situations were reproduced, including driving over the centerline, pedestrian jaywalking, a vehicle cutting in, and intersection traffic signal violation. The Time-To-Collision (TTC) was 2.5 s and the driving speed was set to the common city road speed limits of 60 and 80 km/h. An eye tracker was used to determine the driver’s Saccade Latency (SL) during hazardous situations. Brake Reaction Time from Perception (BRT_P), Steer Reaction Time from Perception (SRT_P), and Driver Reaction Time from Perception (DRT_P) were derived, and the measurements were statistically analyzed to investigate differences by age group, gender, speed, and type of hazardous situation. Most participants were found to avoid collisions by braking first rather than steering for the presented hazardous situations, except for the cutting in situation. Also, to determine a reaction time that would cover most drivers, the 85th percentile of DRT_P was calculated. The 85th percentile of DRT_P was in the range of 0.550 – 0.800 s. Specifically for each hazardous situation, it was 0.650 s for driving over the centerline, 0.800 s for the pedestrian jaywalking, 0.660 s for cutting in, and 0.550 s for the intersection traffic signal violation. For all 4 hazardous situations combined, the 85th percentile of DRT_P was 0.646 s. The findings can be utilized to determine the driver’s likelihood of avoiding accidents when faced with similar hazardous situations.     

Distracted driving caused by voice message apps: A series of experimental studies

Hand-free voice message apps are frequently used by young people while driving. Previous studies have identified voice message apps as a common source of driving distraction. To quantitatively evaluate the factors contributing to driving distractions, three simulated driving experiments were designed using a dual-task experimental paradigm. In Experiment 1, participants completed several common tasks related to voice messages in WeChat with or without manual operations (perceptual-motor distraction). Experiments 2 and 3 further took into consideration the cognitive distraction level, measured by task difficulty and task frequency. The results showed that, in comparison with undistracted driving, the perceptual-motor distraction related to voice message app use significantly (ps < 0.05) weakened young drivers’ driving performance with respect to the standard deviation of lateral position (SDLP) between two cars (0.24 m), response time (0.21 s) and error rate (0.12) to turning lights, and collision percentage (0.54%), similar to the effects induced by non-voice-based apps. There were also significant differences (ps < 0.05) between driving with secondary tasks with and without continuous manual operations in the SDLP between two cars (0.19 m) and in the response time (0.18 s) and error rate (0.10) to turning lights, which indicates that the distracting effect produced by voice-message apps comes from the related manual operations. The effects of cognitive distraction on driving performance mainly depended on task difficulty level. High-difficulty secondary tasks via a voice message app significantly (ps < 0.05) weakened the driving performance in response time (by 0.13 s and 0.13 s compared to low-difficulty and baseline conditions, respectively) and error rate (by 0.07 and 0.07 compared to low-difficulty and baseline conditions, respectively) to turning lights and collision percentage (by 0.90% and 0.80% compared to low-difficulty and baseline conditions, respectively). The findings provide a theoretical reference for analysing the distracting components of voice messages and suggest that drivers should limit the use of these kinds of apps during driving.     

Evaluation of a dynamic blocking concept to mitigate driver distraction: Three simulator studies

In recent years, the number and complexity of in-vehicle infotainment systems has been steadily increasing. While these systems certainly improve the driving experience, they also increase the risk for driver distraction. International standards and guidelines provide methods of measuring this distraction along with test criteria that help automakers decide whether an interface task is too distracting to be used while driving. Any specific function failing this test should therefore be locked out for use by the driver. This study implemented and tested a dynamic approach to this blocking by algorithmically reacting to driver inputs and the pace of the interaction in order to prevent drivers from having prolonged or too intense sequences of in-vehicle interactions not directly related to driving. Three simulated driving experiments in Germany and the United States were conducted to evaluate this dynamic function blocking concept and also cater for differences in the status quo of either no blocking or static blocking. The experiments consisted of a car following scenario with various secondary interface tasks and always included a baseline condition where no blocking occurred as well as an implementation of the dynamic function blocking. While Experiments 1 and 3 were aimed at collecting and analyzing gaze and driving data from more than 20 participants, Experiment 2 focused on the user experience evaluation of different visual feedback implementations from 13 participants. The user experience as rated by these participants increased throughout the course of all three studies and helped further improve both the concept and feedback design. In the experiments the total glance time towards the road was significantly higher in the dynamic function blocking condition compared to the baseline, already accounting for the increase in total task time inherent to the dynamic condition. Participants developed two strategies of interacting with the dynamic function blocking. They either operated at their normal baseline speed and incurred task blockings or operated slower to avoid the blockings. In the latter strategy, participants chunked their interactions into smaller steps with the present data suggesting that they used the pauses in between chunks to look back onto the road ahead. Theoretical and practical implications of this first evaluation of a dynamic function blocking concept are discussed.     

Investigating the effectiveness of perceptual treatments on a crest vertical curve: A driving simulator study

Rural roads are characterized by a high percentage of run-off-the-road accidents and head-on collisions, mainly caused by inappropriate speeds and failure to maintain a proper lateral position along the roadway alignment. Among several road safety treatments, low-cost perceptual measures are considered an effective tool, as they generally increase the risk perceived by drivers, or even alter the drivers’ speed perception, and consequently tempting them to decrease their speeds. Their effectiveness has been widely recognized in a number of studies, especially with respect to road intersections and curves.The overall aim of this study is to investigate the effects of different perceptual treatments on driving speed, along a crest vertical curve of an existing two-lane rural road, in order to identify the most effective measure to reduce speed and define its subsequent implementation in the field. Three perceptual treatments were tested using a driving simulator: white peripheral transverse bars, red peripheral transverse bars and optical speed bars, with each one being painted along the approaching tangent to the crest vertical curve. The effects of these speed-reducing measures were investigated using a sample of forty-four participants, by comparing the driving speeds with those recorded under a baseline condition (without a treatment); these were also used to validate the driving simulator’s speed measurements with those found in the field. Moreover, subjective measures were collected, consisting of the driver’s static evaluation of the desired speed, risk perception and markings comprehension, based on screen shot pictures that represented the simulated configurations of the treatments.The findings demonstrated an overall effectiveness of the perceptual treatments, although only the red peripheral transverse bars were found to significantly reduce the driving speeds (−6 km/h). The analysis of the questionnaire yielded interesting information and demonstrated the importance of performing driving simulation tests for evaluating the effectiveness of perceptual treatments.Finally, the results confirmed the enormous potential of using driving simulators to pinpoint a number of speed-reducing measures, and consequently select the most effective one that reduces cost and promotes safety before its actual implementation in the field.     

Effects of visual factors during automated driving of mobility scooters on user comfort: An exploratory simulator study

An automated mobility scooter is expected to provide convenient and safe transportation for users in their living area. However, there is limited research on user comfort compared to that on user safety for the automated driving of mobility scooters. Because the user does not perform driving tasks in automated driving, the visual information from the peripheral environment and visual behavior is expected to closely affect the psychological comfort of the user. This study clarifies the effects of factors related to the automated driving of mobility scooters and the peripheral environment on the visual behavior and psychological comfort of the user. Effects of driving velocity and pedestrian density on the visual behavior and psychophysiological responses of users were investigated via a driving simulator. The results showed that automated driving in an environment with a high pedestrian density can result in a decrease in fixation duration, deactivation of visual processing, sympathetic activation, and feeling of negative emotion. This implies that the assessment of visual behaviors of users is important for the design of automated mobility scooters to improve user comfort.     

Car-following behavioural adaptation when driving next to automated vehicles on a dedicated lane on motorways: A driving simulator study in the Netherlands

Automated vehicles (AVs) are expected to improve traffic flow efficiency and safety. The deployment of AVs on motorways is expected to be the first step in their implementation. One of the main concerns is how human drivers will interact with AVs. Dedicating specific lanes to AVs have been suggested as a possible solution. However, there is still a lack of evidence-based research on the consequence of dedicated lanes for AVs on human drivers’ behavior. To bridge this research gap, a driving simulator experiment was conducted to investigate the behavior of human drivers exposed to different road design configurations of dedicated lanes on motorways. The experiment sample consisted of 34 (13 female) licensed drivers in the age range of 20–30. A repeated measures ANOVA was applied, which revealed that the type of separation between the dedicated lane and the other lanes has a significant influence on the behavior of human drivers driving in the proximity of AV platoons. Human drivers maintained a significantly lower time headway (THW) when driving in the proximity of a continuous access dedicated lane as compared to a limited-access dedicated lane with a guardrail separation for AV platoons. A similar result was found for the limited-access dedicated lane in comparison to the limited-access dedicated lane with guardrail separation. Moreover, the results regarding the empirical relationships between THW and sociodemographic variables indicate a significant THW difference between males and females as well as a significant inverse relationship between THW and the years of driving experience.     

Driving distraction at night: The impact of cell phone use on driving behaviors among young drivers

Currently, young drivers are more likely than other drivers to use cell phones while driving at night, which has become a major cause of road crashes. However, limited attention has been given to distracted nighttime driving. Therefore, the aim of this study was to explore the interaction effect of cell phone use and time of day (daytime and nighttime) on young drivers’ car-following performance. Forty-three young drivers engaged in a driving simulator experiment with a within-subject design that included three distractions (no distraction, talking and texting on a cell phone) and two times of day. This paper applied non-parametric tests to analyze the data and obtained the following results: (1) the standard deviation of lane position (SDLP) did not significantly differ at either time of day under no distraction, but it was significantly higher at night on straight roads and large-radius curves after introducing distractions. In addition, participants drove faster and gave less headway on small-radius curves under both distractions at night; (2) texting significantly increased the SDLP, while there was less lateral variation during the talking tasks than under no distraction on simple road sections; and (3) compared with the experienced drivers, the novice drivers drove faster during the talking tasks on small-radius curves, but there was no significant difference between groups during the texting tasks. These findings provide both theoretical and practical implications for related policy makers to enhance traffic safety.     

Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.     

Driver perception hypothesis: Driving simulator study

According the driver perception hypothesis, horizontal curves appear sharper or flatter when overlapping with crest or sag vertical curves, respectively. Confirmations of this hypothesis are provided by studies carried out using non-interactive techniques that do not allow the analysis of the driver’s reactions to the visual perception of the road.This study was aimed to add to the body of knowledge concerning driver’s speed behavior on combined curves, as well as to test the perception hypothesis based on the speed data collected during tests in the interactive CRISS driving simulator.Speeds on the tangent-curve transition of crest and sag combinations were compared to those on the tangent-curve transition of horizontal curves with the same radii but on a flat grade (reference curves).For the crest combinations the results of the statistical analyses were fully consistent with the perception hypothesis. On the sag combinations, on the contrary, the driver’s speed behavior did not differ in any statistically significant way from that on the reference curves. Therefore this finding did not support the perception hypothesis on the sag combinations. The effects of the combined curves on the driver’s speed behavior did not change in function of the level of the radius. Some implications of these findings have been highlighted.     

The ability of young,middle-aged and older drivers to inhibit visual and auditory distraction in a driving simulator task

Driver distraction is one major cause of road traffic accidents. In order to avoid distraction-related accidents it is important to inhibit irrelevant stimuli and unnecessary responses to distractors and to focus on the driving task, especially when unpredictable critical events occur. Since inhibition is a cognitive function that develops until young adulthood and decreases with increasing age, young and older drivers should be more susceptible to distraction than middle-aged drivers. Using a driving simulation, the present study investigated effects of acoustic and visual distracting stimuli on responses to critical events (flashing up brake lights of a car ahead) in young, middle-aged, and older drivers. The task difficulty was varied in three conditions, in which distractors could either be ignored (perception-only), or required a simple response (detection) or a complex Go-/NoGo-response (discrimination). Response times and error rates to the critical event increased when a simultaneous reaction to the distractor was required. This distraction effect was most pronounced in the discrimination condition, in which the participants had to respond to some of the distracting stimuli and to inhibit responses to some other stimuli. Visual distractors had a stronger impact than acoustic ones. While middle-aged drivers managed distractor inhibition even in difficult tasks quite well (i.e., when responses to distracting stimuli had to be suppressed), response times of young and old drivers increased significantly, especially when distractor stimuli had to be ignored. The results demonstrate the high impact of distraction on driving performance in critical traffic situations and indicate a driving-related inhibition deficit in young and old drivers.     

Driving demands,stress reactivity and driving behavior: An interactional approach

The aim of the present study was to investigate the role of driving demands, neuroticism, and their interaction when predicting driving behavior. More precisely, we strived to examine how driving behaviors (i.e., speeding, winding, tailgating and jerky driving) unfold across low and high driving demands and whether they are contingent on a personality factor that has previously been linked to stress reactivity. In a driving simulator, 50 participants with a valid driver’s license (56.6% female, age: M = 30.13, SD = 10.16) were exposed to driving scenarios of different levels of information processing and vehicle handling demands. Additionally, they filled-out a self-report questionnaire that measured their neuroticism. We found that driving behavior became safer in scenarios that were highly demanding in terms of information processing, while this pattern did not emerge with vehicle handling demands. Moreover, tentative support was found for the notion that individuals high in neuroticism are less able to adapt their behavior to higher information processing demands. The present study offers new insights on driving demands in a simulated driving context and points to the potential importance of exploring interactions between personality and situational factors when understanding driving behavior. Additionally, the results of the present study may be used to adapt driver’s education programs.     

Keep calm,pay attention,and carry on: Anxiety and consciousness mediate the effect of,mindfulness on driving performance in young drivers

Road traffic crashes are currently one of the main causes of deaths in the world and many efforts have been made to develop effective interventions to reduce them. Mindfulness has risen as a method for improving mental and physical well-being and has been hypothesized as potentially beneficial for driving performance. This has led to some commercial ventures based on such hypothesis, despite that the empirical evidence backing up them is still limited. Besides, at the moment there is not yet a clear account of the specific mechanism underlying this proposals. So, it seems plausible that the relationship between mindfulness and driving performance is indirect, and that personality traits such as conscientiousness and neuroticism may play a mediation role between mindfulness and driving performance. A sample of 98 drivers between the ages of 19 and 29 completed questionnaires assessing mindfulness, anxiety trait and anxiety state, and the big five personality traits. Driving performance was assessed in a driving simulator. A mediation model was fitted with conscientiousness and neuroticism set as mediators of the effects of the relationship between the subject’s mindfulness levels and the driving performance.