Enhancing Driving Safety through User Experience Evaluation of the C-ITS Mobile Application: A Case Study of the DARS Traffic Plus App in a Driving Simulator Environment.

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers' safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app's efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.

Principal Components Analysis of Driving Simulator Variables in Novice Drivers.

Objective: Although driving simulators are powerful tools capable of measuring a wide-ranging set of tactical and operational level driving behaviors, comparing these behaviors across studies is problematic because there is no core set of driving variables to report when assessing driving behavior in simulated driving scenarios. To facilitate comparisons across studies, researchers need consistency in how driving simulator variables combine to assess driving behavior. With inter-study consistency, driving simulator research could support stronger conclusions about safe driving behaviors and more reliably identify future driver training goals. The purpose of the current study was to derive empirically and theoretically meaningful composite scores from driving behaviors of young people in a driving simulator, utilizing driving data from across a variety of driving environments and from within the individual driving environments. Method: One hundred ninety adolescent participants aged 16 years or 18 years at enrollment provided demographic data and drove in a high-fidelity driving simulator. The simulated scenario included 4 distinct environments: Urban, Freeway, Residential, and a Car Following Task (CFT). A Principal Components Analysis (PCA) was conducted on the variable output from the driving simulator to select optimal factor solutions and loadings both across the multi-environmental drive and within the four individual driving environments. Results: The PCA suggested two components from the multi-environmental simulated drive: vehicle control and speed. The individual driving environments also indicated two components: vehicle control and tactical judgment. Conclusion: These findings are among the first steps for identifying composite driving simulator variables to quantify theoretical conceptualizations of driving behavior. Currently, driving behavior and performance measured by driving simulators lack "gold standards" via driving scores or benchmarks. The composites derived in this analysis may be studied for further use where driving behavior standards are increasingly sought by clinicians and practitioners for a variety of populations, as well as by parents concerned about the readiness of their novice driving teen.

Long-term effects of daylight saving time on driving fatigue.

The study of the relationship between Daylight Saving Time (DST) and road safety has yielded contrasting results, most likely in relation to the inability of crash-database approaches to unravel positive (ambient lighting-related) and negative (circadian/sleep-related) effects, and to significant geographical differences in lighting-related effects. The aim of this study was to investigate the effects of DST on driving fatigue, as measured by driving-based, physiological and subjective indicators obtained from a driving simulator experiment. Thirty-seven participants (73 % males, 23 ± 2 years) completed a series of 50-min trials in a monotonous highway environment: Trial 1 was in the week prior to the Spring DST transition, Trial 2 in the following week, and Trial 3 in the fourth week after the transition. Thirteen participants returned for Trial 4, in the week prior to the Autumn switch to civil time, and Trial 5 in the following week. Significant adverse effects of DST on vehicle lateral control and eyelid closure were documented in Trial 2 and Trial 3 compared to Trial 1, with no statistical differences between Trials 2 and 3. Further worsening in vehicle lateral control was documented in Trials 4 and 5. Eyelid closure worsened up to Trial 4, and improved in Trial 5. Participants were unaware of their worsening performance based on subjective indicators. In conclusion, DST has a detrimental impact on driving fatigue during the whole time during which it is in place. Such an impact is comparable, for example, to that associated with driving with a blood alcohol concentration of 0.5 g/L.

Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications.

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

Recognition of aggressive driving behavior under abnormal weather based on Convolutional Neural Network and transfer learning.

OBJECTIVES: Aggressive driving behavior can lead to potential traffic collision risks, and abnormal weather conditions can exacerbate this behavior. This study aims to develop recognition models for aggressive driving under various climate conditions, addressing the challenge of collecting sufficient data in abnormal weather. METHODS: Driving data was collected in a virtual environment using a driving simulator under both normal and abnormal weather conditions. A model was trained on data from normal weather (source domain) and then transferred to foggy and rainy weather conditions (target domains) for retraining and fine-tuning. The K-means algorithm clustered driving behavior instances into three styles: aggressive, normal, and cautious. These clusters were used as labels for each instance in training a CNN model. The pre-trained CNN model was then transferred and fine-tuned for abnormal weather conditions. RESULTS: The transferred models showed improved recognition performance, achieving an accuracy score of 0.81 in both foggy and rainy weather conditions. This surpassed the non-transferred models' accuracy scores of 0.72 and 0.69, respectively. CONCLUSIONS: The study demonstrates the significant application value of transfer learning in recognizing aggressive driving behaviors with limited data. It also highlights the feasibility of using this approach to address the challenges of driving behavior recognition under abnormal weather conditions.

A systematic review of major evaluation metrics for simulator-based automatic assessment of driving after stroke.

Background: Simulator-based driving assessments (SA) have recently been used and studied for various purposes, particularly for post-stroke patients. Automating such assessment has potential benefits especially on reducing financial cost and time. Nevertheless, there currently exists no clear guideline on assessment techniques and metrics available for SA for post-stroke patients. Therefore, this systematic review is conducted to explore such techniques and establish guidelines for evaluation metrics. Objective: This review aims to find: (a) major evaluation metrics for automatic SA in post-stroke patients and (b) assessment inputs and techniques for such evaluation metrics. Methods: The study follows the PRISMA guideline. Systematic searches were performed on PubMed, Web of Science, ScienceDirect, ACM Digital Library, and IEEE Xplore Digital Library for articles published from January 1, 2010, to December 31, 2023. This review targeted journal articles written in English about automatic performance assessment of simulator-based driving by post-stroke patients. A narrative synthesis was provided for the included studies. Results: The review included six articles with a total of 239 participants. Across all of the included studies, we discovered 49 distinct assessment inputs. Threshold-based, machine-learning-based, and driving simulator calculation approaches are three primary types of assessment techniques and evaluation metrics identified in the review. Discussion: Most studies incorporated more than one type of input, indicating the importance of a comprehensive evaluation of driving abilities. Threshold-based techniques and metrics were the most commonly used in all studies, likely due to their simplicity. An existing relevant review also highlighted the limited number of studies in this area, underscoring the need for further research to establish the validity and effectiveness of simulator-based automatic assessment of driving (SAAD). Conclusions: More studies should be conducted on various aspects of SAAD to explore and validate this type of assessment.

Driving performance of long-term users of sedating antidepressants and benzodiazepines.

OBJECTIVE: Using benzodiazepines and certain antidepressants is associated with an increased risk of motor vehicle crashes due to impaired driving skills. Hence, several countries prohibit people who use these drugs from driving. Traffic regulations for driving under the influence of these drugs are, however, largely based on single-dose studies with healthy participants. The effects of drugs on chronic users may be different because of potential development of tolerance or by adapting behavior. In this study, we test the effects of anti-depressants, hypnotics, or anxiolytics use on driving performance in patients who use these drugs for different durations and compare the effects to healthy controls' performance. METHODS: Sixty-six healthy controls and 82 medication users were recruited to perform four drives in a driving simulator. Patients were divided into groups that used anti-depressants, hypnotics, or anxiolytics, for shorter or longer than 3 years (i.e. LT3- or LT3+, respectively). The minimum term of use was 6 months. Driving behavior was measured in terms of longitudinal and lateral control (speed variability and Standard Deviation of Lateral Position: SDLP), brake reaction time, and time headway. Impaired driving performance was defined as performing similar to driving with a Blood Alcohol Concentration of 0.5‰ or higher, determined by means of non-inferiority analyses. RESULTS: Reaction time analyses revealed inconclusive findings in all groups. No significant performance differences between matched healthy controls, LT3- (n = 2), and LT3+ (n = 8) anxiolytics users were found. LT3+ antidepressants users (n = 12) did not perform inferior to their matched controls in terms of SDLP. LT3- hypnotics users (n = 6) showed more speed variability than their matched healthy controls, while this effect was not found for the LT3+ group (n = 14): the latter did not perform inferior to the healthy controls. Regarding Time Headway, no conclusions about the LT3- hypnotics group could be drawn, while the LT3+ group did not perform inferior compared to the control group. CONCLUSIONS: The small number of anxiolytics users prohibits drawing conclusions about clinical relevance. Although many outcomes were inconclusive, there is evidence that some elements of complex driving performance may not be impaired (anymore) after using antidepressants or hypnotics longer than 3 years.

Gap acceptance behaviour and crash risks of mobile phone distracted young drivers at roundabouts: A random parameters survival model.

Navigating through complex road geometries, such as roundabouts, poses significant challenges and safety risks for drivers. These challenges may be exacerbated when drivers are distracted by mobile phone conversations. The interplay of road geometry, driving state, and driver characteristics in creating compound risks remains an underexplored area in existing literature. Proper understanding of such compound crash risk is not only crucial to improve road geometric design but also to educate young drivers, who are particularly risk-takers and to devise strict penalties for mobile phone usage whilst driving. To fill this gap, this study examines crash risks associated with gap acceptance manoeuvres at roundabouts in the simulated environment of the CARRS-Q driving simulators, where 32 licenced young drivers were exposed to a gap acceptance scenario in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A parametric random parameters survival modelling approach is adopted to understand safety margins-characterised by gap times-during gap acceptance scenarios at roundabouts, concurrently uncover driver-level heterogeneity with mobile phone distraction and capture repeated measures of experiment design. The model specification includes the handheld phone condition as a random parameter and hands-free phone condition, acceleration noise, gap size, crash history, and gender as non-random parameters. Results suggest that the majority of handheld distracted drivers have smaller safety margins, reflecting the negative consequences of engaging in handheld phone conversations. Interestingly, a group of drivers in the same handheld phone condition have been found to exhibit cautious/safer behaviour, as evidenced by longer gap times, reflecting their risk compensation behaviour. Female distracted drivers are also found to exhibit safer gap acceptance behaviour compared to distracted male drivers. The findings of this study shed light on the compound risk of mobile phone distraction and gap acceptance at roundabouts, requiring policymakers and authorities to devise strict penalties and laws for distracted driving.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study.

BACKGROUND: Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear. OBJECTIVE: This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare. METHODS: Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions. RESULTS: Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions. CONCLUSIONS: Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an Explainable Classifier.

The transition to fully autonomous roadways will include a long period of mixed-autonomy traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs) which use conservative driving behaviours to safely negotiate complex scenarios. This can lead to congestion and collisions with human drivers who are accustomed to more confident driving styles. In this work, an explainable multi-variate time series classifier, Time Series Forest (TSF), is compared to two state-of-the-art models in a priority-taking classification task. Responses to left-turning hazards at signalized and stop-sign-controlled intersections were collected using a full-vehicle driving simulator. The dataset was comprised of a combination of AV sensor-collected and V2V (vehicle-to-vehicle) transmitted features. Each scenario forced participants to either take ("go") or yield ("no go") priority at the intersection. TSF performed comparably for both the signalized and sign-controlled datasets, although all classifiers performed better on the signalized dataset. The inclusion of V2V data led to a slight increase in accuracy for all models and a substantial increase in the true positive rate of the stop-sign-controlled models. Additionally, incorporating the V2V data resulted in fewer chosen features, thereby decreasing the model complexity while maintaining accuracy. Including the selected features in an AV planning model is hypothesized to reduce the need for conservative AV driving behaviour without increasing the risk of collision.

Post-Takeover Proficiency in Conditionally Automated Driving: Understanding Stabilization Time with Driving and Physiological Signals.

In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experiments. By analyzing both driving and physiological signals, we investigate the time required for the driver to regain full control and adapt to the dynamic driving task following automation. Our findings show that the stabilization time varies between measured parameters. While the drivers achieved driving-related stabilization (winding, speed) in eight to ten seconds, physiological parameters (heart rate, phasic skin conductance) exhibited a prolonged response. By elucidating the temporal and cognitive dynamics underlying the stabilization process, our results pave the way for the development of more effective and user-friendly automated driving systems, ultimately enhancing safety and driving experience on the roads.

Predicting driver's takeover time based on individual characteristics, external environment, and situation awareness.

The driver's takeover time is crucial to ensure a safe takeover transition in conditional automated driving. The study aimed to construct a prediction model of driver's takeover time based on individual characteristics, external environment, and situation awareness variables. A total of 18 takeover events were designed with scenarios, non-driving-related tasks, takeover request time, and traffic flow as variables. High-fidelity driving simulation experiments were carried out, through which the driver's takeover data was obtained. Fifteen basic factors and three dynamic factors were extracted from individual characteristics, external environment, and situation awareness. In this experiment, these 18 factors were selected as input variables, and XGBoost and Shapely were used as prediction methods. A takeover time prediction model (BM + SA model) was then constructed. Moreover, we analyzed the main effect of input variables on takeover time, and the interactive contribution made by the variables. And in this experiment, the 15 basic factors were selected as input variables, and the basic takeover time prediction model (BM model) was constructed. In addition, this study compared the performance of the two models and analyzed the contribution of input variables to takeover time. The results showed that the goodness of fit of the BM + SA model (Adjusted_R2) was 0.7746. The XGBoost model performs better than other models (support vector machine, random forest, CatBoost, and LightBoost models). The relative importance degree of situation awareness variables, individual characteristic variables, and external environment variables to takeover time gradually reduced. Takeover time increased with the scan and gaze durations and decreased with pupil area and self-reported situation awareness scores. There was also an interaction effect between the variables to affect takeover time. Overall, the performance of the BM + SA model was better than that of the BM model. This study can provide support for predicting driver's takeover time and analyzing the mechanism of influence on takeover time. This study can provide support for the development of real-time driver's takeover ability prediction systems and optimization of human-machine interaction design in automated vehicles, as well as for the management department to evaluate and improve the driver's takeover performance in a targeted manner.

Calibrated to drive: Measuring self-assessed driving ability and perceived workload after prolonged sitting and sleep restriction.

Self-assessed driving ability may differ from actual driving performance, leading to poor calibration (i.e., differences between self-assessed driving ability and actual performance), increased risk of accidents and unsafe driving behaviour. Factors such as sleep restriction and sedentary behaviour can impact driver workload, which influences driver calibration. This study aims to investigate how sleep restriction and prolonged sitting impact driver workload and driver calibration to identify strategies that can lead to safer and better calibrated drivers. Participants (n = 84, mean age = 23.5 ± 4.8, 49 % female) undertook a 7-day laboratory study and were randomly allocated to a condition: sitting 9-h sleep opportunity (Sit9), breaking up sitting 9-h sleep opportunity (Break9), sitting 5-h sleep opportunity (Sit5) and breaking up sitting 5-h sleep opportunity (Break5). Break9 and Break5 conditions completed 3-min of light-intensity walking on a treadmill every 30 min between 09:00-17:00 h, while participants in Sit9 and Sit5 conditions remained seated. Each participant completed a 20-min simulated commute in the morning and afternoon each day and completed subjective assessments of driving ability and perceived workload before and after each commute. Objective driving performance was assessed using a driving simulator measuring speed and lane performance metrics. Driver calibration was analysed using a single component and 3-component Brier Score. Correlational matrices were conducted as an exploratory analysis to understand the strength and direction of the relationship between subjective and objective driving outcomes. Analyses revealed participants in Sit9 and Break9 were significantly better calibrated for lane variability, lane position and safe zone-lane parameters at both time points (p < 0.0001) compared to Sit5 and Break5. Break5 participants were better calibrated for safe zone-speed and combined safe zone parameters (p < 0.0001) and speed variability at both time points (p = 0.005) compared to all other conditions. Analyses revealed lower perceived workload scores at both time points for Sit9 and Break9 participants compared to Sit5 and Break5 (p = <0.001). Breaking up sitting during the day may reduce calibration errors compared to sitting during the day for speed keeping parameters. Future studies should investigate if different physical activity frequency and intensity can reduce calibration errors, and better align a driver's self-assessment with their actual performance.

Neuroanatomical correlates of distracted straight driving performance: a driving simulator MRI study across the lifespan.

Background: Driving is the preferred mode of transportation for adults across the healthy age span. However, motor vehicle crashes are among the leading causes of injury and death, especially for older adults, and under distracted driving conditions. Understanding the neuroanatomical basis of driving may inform interventions that minimize crashes. This exploratory study examined the neuroanatomical correlates of undistracted and distracted simulated straight driving. Methods: One-hundred-and-thirty-eight participants (40.6% female) aged 17-85 years old (mean and SD = 58.1 ± 19.9 years) performed a simulated driving task involving straight driving and turns at intersections in a city environment using a steering wheel and foot pedals. During some straight driving segments, participants responded to auditory questions to simulate distracted driving. Anatomical T1-weighted MRI was used to quantify grey matter volume and cortical thickness for five brain regions: the middle frontal gyrus (MFG), precentral gyrus (PG), superior temporal cortex (STC), posterior parietal cortex (PPC), and cerebellum. Partial correlations controlling for age and sex were used to explore relationships between neuroanatomical measures and straight driving behavior, including speed, acceleration, lane position, heading angle, and time speeding or off-center. Effects of interest were noted at an unadjusted p-value threshold of 0.05. Results: Distracted driving was associated with changes in most measures of straight driving performance. Greater volume and cortical thickness in the PPC and cerebellum were associated with reduced variability in lane position and heading angle during distracted straight driving. Cortical thickness of the MFG, PG, PPC, and STC were associated with speed and acceleration, often in an age-dependent manner. Conclusion: Posterior regions were correlated with lane maintenance whereas anterior and posterior regions were correlated with speed and acceleration, especially during distracted driving. The regions involved and their role in straight driving may change with age, particularly during distracted driving as observed in older adults. Further studies should investigate the relationship between distracted driving and the aging brain to inform driving interventions.

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea.

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta-alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta-alpha-ratio (87.2%) and delta-theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta-alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective.

Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points.This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers' characteristics (i.e., driving experience) were examined.In total, 43 participants, aged 19-56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers' gaze direction and fixation on nine areas of interest were captured with an eye tracker. Results: showed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers' attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.

Evaluation of power wheelchair driving performance in simulator compared to driving in real-life situations: the SIMADAPT (simulator ADAPT) project-a pilot study.

OBJECTIVE: The objective of this study was to evaluate users' driving performances with a Power Wheelchair (PWC) driving simulator in comparison to the same driving task in real conditions with a standard power wheelchair. METHODS: Three driving circuits of progressive difficulty levels (C1, C2, C3) that were elaborated to assess the driving performances with PWC in indoor situations, were used in this study. These circuits have been modeled in a 3D Virtual Environment to replicate the three driving task scenarios in Virtual Reality (VR). Users were asked to complete the three circuits with respect to two testing conditions during three successive sessions, i.e. in VR and on a real circuit (R). During each session, users completed the two conditions. Driving performances were evaluated using the number of collisions and time to complete the circuit. In addition, driving ability by Wheelchair Skill Test (WST) and mental load were assessed in both conditions. Cybersickness, user satisfaction and sense of presence were measured in VR. The conditions R and VR were randomized. RESULTS: Thirty-one participants with neurological disorders and expert wheelchair drivers were included in the study. The driving performances between VR and R conditions were statistically different for the C3 circuit but were not statistically different for the two easiest circuits C1 and C2. The results of the WST was not statistically different in C1, C2 and C3. The mental load was higher in VR than in R condition. The general sense of presence was reported as acceptable (mean value of 4.6 out of 6) for all the participants, and the cybersickness was reported as acceptable (SSQ mean value of 4.25 on the three circuits in VR condition). CONCLUSION: Driving performances were statistically different in the most complicated circuit C3 with an increased number of collisions in VR, but were not statistically different for the two easiest circuits C1 and C2 in R and VR conditions. In addition, there were no significant adverse effects such as cybersickness. The results show the value of the simulator for driving training applications. Still, the mental load was higher in VR than in R condition, thus mitigating the potential for use with people with cognitive disorders. Further studies should be conducted to assess the quality of skill transfer for novice drivers from the simulator to the real world. Trial registration Ethical approval n ∘ 2019-A001306-51 from Comité de Protection des Personnes Sud Mediterranée IV. Trial registered the 19/11/2019 on ClinicalTrials.gov in ID: NCT04171973.

Effects of kratom on driving: Results from a cross-sectional survey, ecological momentary assessment, and pilot simulated driving Study.

OBJECTIVES: Despite widespread kratom use, there is a lack of knowledge regarding its effects on driving. We evaluated the self-reported driving behaviors of kratom consumers and assessed their simulated-driving performance after self-administering kratom products. METHODS: We present results from: 1) a remote, national study of US adults who regularly use kratom, and 2) an in-person substudy from which we re-recruited participants. In the national study (N = 357), participants completed a detailed survey and a 15-day ecological momentary assessment (EMA) that monitored naturalistic kratom use. For the remote study, outcomes were self-reported general and risky driving behaviors, perceived impairment, and driving confidence following kratom administration. For the in-person substudy, 10 adults consumed their typical kratom products and their driving performance on a high-fidelity driving simulator pre- and post-kratom administration was evaluated. RESULTS: Over 90% of participants surveyed self-reported driving under the influence of kratom. Most reported low rates of risky driving behavior and expressed high confidence in their driving ability after taking kratom. This was consistent with EMA findings: participants reported feeling confident in their driving ability and perceived little impairment within 15-180 min after using kratom. In the in-person substudy, there were no significant changes in simulated driving performance after taking kratom. CONCLUSIONS: Using kratom before driving appears routine, however, self-reported and simulated driving findings suggest kratom effects at self-selected doses among regular kratom consumers do not produce significant changes in subjective and objective measures of driving impairment. Research is needed to objectively characterize kratom's impact on driving in regular and infrequent consumers.

R2DRV: study protocol for longitudinal assessment of driving after mild TBI in young drivers.

BACKGROUND: Mild traumatic brain injury (mTBI) and traffic-related injuries are two major public health problems disproportionately affecting young people. Young drivers, whose driving skills are still developing, are particularly vulnerable to impaired driving due to brain injuries. Despite this, there is a paucity of research on how mTBI impacts driving and when it is safe to return to drive after an mTBI. This paper describes the protocol of the study, R2DRV, Longitudinal Assessment of Driving After Mild TBI in Young Drivers, which examines the trajectory of simulated driving performance and self-reported driving behaviors from acutely post-injury to symptom resolution among young drivers with mTBI compared to matched healthy drivers. Additionally, this study investigates the associations of acute post-injury neurocognitive function and cognitive load with driving among young drivers with and without mTBI. METHODS: A total of 200 young drivers (ages 16 to 24) are enrolled from two study sites, including 100 (50 per site) with a physician-confirmed isolated mTBI, along with 100 (50 per site) healthy drivers without a history of TBI matched 1:1 for age, sex, driving experience, and athlete status. The study assesses primary driving outcomes using two approaches: (1) high-fidelity driving simulators to evaluate driving performance across four experimental study conditions at multiple time points (within 96 h of injury and weekly until symptom resolution or 8 weeks post-injury); (2) daily self-report surveys on real-world driving behaviors completed by all participants. DISCUSSION: This study will fill critical knowledge gaps by longitudinally assessing driving performance and behaviors in young drivers with mTBI, as compared to matched healthy drivers, from acutely post-injury to symptom resolution. The research strategy enables evaluating how increased cognitive load may exacerbate the effects of mTBI on driving, and how post-mTBI neurocognitive deficits may impact the driving ability of young drivers. Findings will be shared through scientific conferences, peer-reviewed journals, and media outreach to care providers and the public.

Factors affecting driving performance in patients with Multiple Sclerosis - still an open question.

Background and objectives: Research on driving ability in people with multiple sclerosis (MS) suggests that they might be at risk for unsafe driving due to MS-related motor, visual, and cognitive impairment. Our first aim was to investigate differences in driving ability and performance between people with MS (PwMS) and those without any neurologic or psychiatric disease ("controls"). Secondly, we determined disease-related factors influencing driving ability in PwMS. Methods: We prospectively compared standardized performance in a driving simulator between 97 persons with early MS [mean (SD) = 6.4 (7.3) years since diagnosis, mean (SD) Expanded Disability Status Scale (EDSS) = 2.5 (1.4)] and 94 group-matched controls. Participants completed an extensive examination comprising questionnaires and assessments regarding driving, cognitive and psychological factors, as well as demographic and disease-related measures. Between-group comparisons of driving-relevant neuropsychological tests and driving performance were done. Correlations were performed to define demographic and disease-related factors on driving performance in MS. Results: In a driving simulator setting, PwMS had more driving accidents [T(188) = 2.762, p = 0.006], reacted slower to hazardous events [T(188) = 2.561, p = 0.011], made more driving errors [T(188) = 2.883, p = 0.004] and had a worse Driving Safety Score (DSS) [T(188) = 3.058, p = 0.003] than controls. The only disease-related measure to be associated with most driving outcomes was the Wechsler Block-Tapping test (WMS-R) backward: number of accidents (r = 0.28, p = 0.01), number of driving errors (r = 0.23, p = 0.05) and DSS (r = -0.23, p = 0.05). Conclusion: Driving performance in a simulator seems to be reduced in PwMS at an early stage of disease compared to controls, as a result of increased erroneous driving, reduced reaction time and higher accident rate. MS-related impairment in mobility, vision, cognition, and in psychological and demographic aspects showed no or only minimal association to driving ability, but impairment in different areas of cognition such as spatial short-term memory, working memory and selective attention correlated with the number of accidents, and might indicate a higher risk for driving errors and worse performance. These results show that driving ability is a complex skill with involvement of many different domains, which need further research.