Driver Crash Risk Research Articles

Identifying factors affecting driver takeover time and crash risk during the automated driving takeover process

This study aims to develop prediction models of driver takeover time and crash risks during the automated driving takeover process. A driving simulator experiment was conducted to collect vehicle trajectory and driver behavior data. The random-parameter duration model was first built to model driver takeover time. Results indicated that young drivers, novice drivers, takeover request lead time, and traffic volume had varying impacts on takeover time due to the unobserved heterogeneity. Then, an explainable machine learning model was utilized to predict and explore various predictors’ impacts on takeover crashes. Validation results revealed that the developed model provided satisfactory accuracy in predicting crashes. SHAP was used to interpret the estimated results by examining contributory factors’ main effects and interactive effects on crash risks. Takeover crash risk is positively correlated with vehicle speed, takeover time, maximum lateral acceleration, traffic volume, and tasks of watching videos and playing games. Additionally, takeover request lead time and the maximum longitudinal deceleration were found to affect crash risks negatively. Research findings shed insights into modeling takeover time and predicting crash risks during the takeover process, and highlight the importance of considering the heterogeneity of drivers when designing automated driving systems (ADS) to improve driver takeover performance.

Syncope While Driving and the Risk of a Subsequent Motor Vehicle Crash

Syncope that occurs while driving can result in a motor vehicle crash. Whether individuals with a prior syncope-related crash exhibit an exceptional risk of subsequent crash remains uncertain. We performed a population-based retrospective observational study of patients diagnosed with 'syncope and collapse' at any of 6 emergency departments in British Columbia, Canada (2010 to 2015). Data were obtained from chart abstraction, administrative health records, insurance claims and police crash reports. We compared crash-free survival among individuals with crash-associated syncope (a crash and an emergency visit for syncope on the same date) to that among controls with syncope alone (no crash on date of emergency visit for syncope). In the year following their index emergency visit, 13 of 63 drivers with crash-associated syncope and 852 of 9,160 controls with syncope alone experienced a subsequent crash as a driver (crash risk 21% versus 9%). After accounting for censoring and potential confounders, crash-associated syncope was not associated with a significant increase in the risk of subsequent crash (adjusted hazard ratio [aHR] 1.38, 95% confidence interval [CI] 0.78 to 2.47). Individuals with crash-associated syncope were 31-fold more likely to have physician driving advice documented during their index visit (prevalence ratio 31.0, 95% CI, 21.3 to 45.1). In the subgroup without documented driving advice, crash-associated syncope was associated with a significant increase in subsequent crash risk (aHR 1.88, 95% CI 1.06 to 3.36). Crash risk after crash-associated syncope appears similar to crash risk after syncope alone.

Motor Vehicle Crash Risk in Older Adult Drivers With Attention-Deficit/Hyperactivity Disorder

Symptoms of attention-deficit/hyperactivity disorder (ADHD), such as inattentiveness and impulsivity, could affect daily functioning and driving performance throughout the life span. Previous research on ADHD and driving safety is largely limited to adolescents and young adults. To examine the prevalence of ADHD and the association between ADHD and crash risk among older adult drivers. This prospective cohort study collected data from primary care clinics and residential communities in 5 US sites (Ann Arbor, Michigan; Baltimore, Maryland; Cooperstown, New York; Denver, Colorado; and San Diego, California) between July 6, 2015, and March 31, 2019. Participants were active drivers aged 65 to 79 years at baseline enrolled in the Longitudinal Research on Aging Drivers project who were studied for up to 44 months through in-vehicle data recording devices and annual assessments. The data analysis was performed between July 15, 2022, and August 14, 2023. Lifetime ADHD based on an affirmative response to the question of whether the participant had ever had ADHD or had ever been told by a physician or other health professional that he or she had ADHD. The main outcomes were hard-braking events defined as maneuvers with deceleration rates of 0.4g or greater, self-reported traffic ticket events, and self-reported vehicular crashes. Multivariable negative binomial modeling was used to estimate adjusted incidence rate ratios (aIRRs) and 95% CIs of outcomes according to exposure status. Of the 2832 drivers studied, 1500 (53.0%) were women and 1332 (47.0%) were men with a mean (SD) age of 71 (4) years. The lifetime prevalence of ADHD in the study sample was 2.6%. Older adult drivers with ADHD had significantly higher incidence rates of hard-braking events per 1000 miles than those without ADHD (1.35 [95% CI, 1.30-1.41] vs 1.15 [95% CI, 1.14-1.16]), as well as self-reported traffic ticket events per 1 million miles (22.47 [95% CI, 16.06-31.45] vs 9.74 [95% CI, 8.99-10.55]) and self-reported vehicular crashes per 1 million miles (27.10 [95% CI, 19.95-36.80] vs 13.50 [95% CI, 12.61-14.46]). With adjustment for baseline characteristics, ADHD was associated with a significant 7% increased risk of hard-braking events (aIRR, 1.07; 95% CI, 1.02-1.12), a 102% increased risk of self-reported traffic ticket events (aIRR, 2.02; 95% CI, 1.42-2.88), and a 74% increased risk of self-reported vehicular crashes (aIRR, 1.74; 95% CI, 1.26-2.40). As observed in this prospective cohort study, older adult drivers with ADHD may be at a significantly elevated crash risk compared with their counterparts without ADHD. These findings suggest that effective interventions to improve the diagnosis and clinical management of ADHD among older adults are warranted to promote safe mobility and healthy aging.

A randomized controlled pilot trial of brief online mindfulness training in young drivers

Driver distraction contributes to fatal and injury crashes in young drivers. Mind wandering (MW) is a covert form of distraction involving task-unrelated thoughts. Brief online mindfulness training (MT) may reduce unsafe driving by enhancing recognition (meta-awareness) of MW and reducing its occurrence. This pilot trial tested these proposed mechanisms of MT and explored its specificity of action, effects on driving behaviour in simulation, as well as intervention adherence and acceptability in young drivers. A pre-post (T1, T2), randomized, active placebo-controlled, double-blinded design was used. Twenty-six drivers, aged 21-25, received either brief online MT (experimental) or progressive muscle relaxation (PMR, control) over 4-6days. A custom website blindly conducted randomization, delivered interventions, administered questionnaires, and tracked adherence. At T1 and T2, a simulator measured driving behaviour while participants indicated MW whenever they recognized it, to assess meta-awareness, and when prompted by a thought-probe, to assess overall MW. MT reduced MW while driving in simulation. The MT group reported higher state mindfulness following sessions. Motivation did not account for MW or mindfulness results. MT and meta-awareness were associated with more focus-related steering behaviour. Intervention groups did not significantly differ in adherence or attrition. No severe adverse effects were reported, but MT participants reported more difficulty following intervention instructions. Results support a plausible mechanism of MT for reducing MW-related crash risk (i.e., reduction of MW) in young drivers. This preliminary evidence, alongside promising online adherence and acceptability results, warrants definitive efficacy and effectiveness trials of online MT.

How did the COVID-19 pandemic affect driver speed selection and crash risk on rural freeways?

How did the COVID-19 pandemic affect driver speed selection and crash risk on rural freeways?

Further down the road: The enduring effect of an online training course on novice drivers’ hazard perception skill

Further down the road: The enduring effect of an online training course on novice drivers’ hazard perception skill

Does hearing loss affect the risk of involvement in a motor vehicle crash?

This study systematically reviewed the literature on motor vehicle crash (MVC) risk for drivers with hearing loss in medicine, psychology, and transport databases, that quantify its effect on MVC to enable licensing agencies to make evidence-based decisions on fitness-to-drive hearing standards. 1717 articles were identified of which 563 were duplicates. Twelve studies were retained for full-text review of which four published between 1968 and 2016 were judged to have met all the selection criteria. Three studies demonstrated no significant increase in MVC risk, while one reported a decrease in risk. Although the small number of studies that met all the inclusion criteria is a limitation, the expert panel concluded that the quality of the studies permitted the conclusion that the evidence does not support a relationship between hearing impairment and the risk of an MVC. Driver fitness standards recognise this fact implicitly in that none of the national fitness-to-drive standards selected for comparison applied licence restrictions to non-commercial drivers.

How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas

Traffic crashes are among the leading causes of injuries and fatalities worldwide. The main assumption of this study is that traffic crash rates, injury severity, and driving behaviors differ by the driver’s gender. Utilizing ten years (2011–2020) of data from the Texas Crash Record and Information System database, this study investigates how some of the most prominent driving behaviors leading to crashes and severe injuries (distracted driving, speeding, lane departure, and driving under influence) vary by gender in San Antonio, Texas. The spatial distribution of crashes associated with these driving behaviors by gender is also investigated, as well as the influence of some environmental and temporal variables on crash frequency and injury severity. This study adopted bivariate analysis and logistic regression modeling to identify the effect of different variables on crash occurrence and severity by gender. Male drivers were more likely to be involved in a speeding/DUI/lane departure-related crash and subsequent severe injuries. However, female drivers were slightly more associated with distracted-driving crashes and subsequent injuries. Nighttime, interstate/highway roads, the weekend period, and divider/marked lanes as the primary traffic control significantly increased the crash and injury risk of male drivers. Driving behavior-related crashes were mostly concentrated on some interstate road segments, major intersections, and interchanges. The results from this study can be used by authorities and policy-makers to prioritize the use of limited resources, and to run more effective education campaigns to a targeted audience.

Driving offences and risk of subsequent crash in novice drivers: the DRIVE cohort study 12-year follow-up

BackgroundPenalties are a key component to improve road user safety, but previous studies suggested that they might not be successful in reducing crashing in offending drivers. However, these studies were...

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

With the development of big data technologies, usage-based insurance (UBI) has received considerable attention from insurance companies. UBI products focus on identifying the relationship between the individual driver’s risk and online channel behavior variables from Internet of Vehicles (IoV) data. Although omnichannel information integration has promoted the development of many industries, it has not been used to improve the accuracy of driver risk classification models in insurance industries. This paper investigates the role of combining different channel variables in improving the classification of driver’s risk. Specifically, several models, including logistic regression and three different data mining techniques (neural networks, random forests, and support vector machines), augmented with driving behavior data based on the IoV and offline consumer behavior data collected from 4S (Sale, Spare part, Service, Survey) dealers, are applied to the classification model of risk. The empirical results show that the inclusion of online and offline channel data improves the different risk assessments; results also demonstrate the importance of offline consumer behavior variables in different models. These insights have important implications for insurance companies on UBI pricing strategy and cost management.

Are there sex differences in crash and crash-related injury between men and women? A 13-year cohort study of young drivers in Australia.

BackgroundYoung men have long been known to be disproportionately impacted by road crash and crash-related injury compared to young women and older drivers. However, there is limited insight into how sex differences in crash and crash-related injury changes over time as men and women get older and gain more driving experience. To explore sex differences in crash and crash-related injury, we undertook a sex disaggregated analysis in a large longitudinal cohort of over 20,000 young drivers in New South Wales, Australia, for up to 13 years after they first attained their independent car driver licence.MethodsDRIVE Study survey data from 2003–04 were linked with police, hospital and deaths data up to 2016. Sex differences were analysed using cumulative incidence curves investigating time to first crash and in negative binominal regression models adjusted for driver demographics and crash risk factors.ResultsAfter adjusting for demographics and driving exposure, compared with women, men had 1.25 (95% CI 1.18–1.33), 2.07 (1.75–2.45), 1.28 (95% CI 1.13–1.46), 1.32 (95% CI 1.17–1.50) and 1.59 (95% CI 1.43–1.78) times higher rates of any crash, single vehicle crash, crash on streets with a speed limit of 80 km/h or above, crash in wet conditions and crash in the dark, respectively. By contrast, men were less likely to be involved in crashes that resulted in hospitalisation compared to women 0.73 (95% CI 0.55–0.96).ConclusionsYoung men are at increased risk of crash, and this risk persists as they get older and gain more driving experience. Despite lower risk of crash, women are at higher risk of crash related injury requiring hospitalisation. These differences in men's and women's risk of crash and injury signal the need for better understanding of how sex and/or gender may contribute to risk of crash and injury across the life-course.

Sleep disorders and risk of traffic crashes: A naturalistic driving study analysis

• Effect of sleep disorders on crash likelihood was analyzed using naturalistic driving study data. • Shift work sleep disorder poses greatest crash risk, that increases with aging. • Drivers with sleep disorders were also found to be more inattentive while driving. • Crash risk values were lower than those from self-reported and simulator studies. • Countermeasures targeted at drivers with sleep disorders can help increase attention. Sleep disorders and various acute and chronic medical conditions affect the quality and quantity of an individual’s sleep causing excessive day time sleepiness. This study examined the relationship between clinical sleep disorders (i.e., shift work sleep disorder (SWSD), sleep apnea and insomnia) and crashes using naturalistic driving study data. Using data from 1892 events that occurred in six different US states, random effect binary logistic regression models were developed to estimate crash risk associated with SWSD, sleep apnea and insomnia. The study found that sleep disorders elevate crash risk in drivers. Drivers with SWSD exhibited the highest crash risk (OR = 2.96). Older drivers with SWSD were found to be more vulnerable and prone to crashes than other drivers. The analysis showed that drivers with sleep apnea and insomnia were 29% and 33% more likely, respectively, to be involved in a crash or a near-crash. For drivers with insomnia, factors such as age and sleep quality can elevate the associated crash risk. Decrease in sleep quality increases the likelihood of crash or near crash. The study also found that individuals with a sleep disorder are 29% more likely to be inattentive while driving as compared to drivers without a sleep disorder. The OR values found in this study were lower than those reported in studies using self-reported data and driver simulator experiments. In addition to medical diagnosis and treatment, transportation agencies could also consider alleviation strategies such as providing sufficient rest areas on highways and using technology such as in-vehicle and personal technologies that monitor drowsy in-vehicle behavior to increase driver attention. The use of transportation modes such as taxi, transportation network companies (e.g. Uber), or public transit, can be an alternative for shift workers returning home after completing their shift.

Higher order training supporting competence, autonomy, relatedness (HOT-CAR): A model to improve learner drivers' higher order skills

Abstract Approaches to teaching young Learners to drive on-road often lack a strong, overarching theoretical framework. This paper proposes a transtheoretical model to guide instruction of higher-order skills – that are associated with reduced crash risk in young drivers – including established teaching techniques for effective instruction. Learnings from Self-Determination Theory (SDT) and the Goals for Driver Education framework (widely identified as best-practice but not effectively translated into practice) were integrated into the Higher Order Training supporting Competence, Autonomy, Relatedness (HOT-CAR) model. The model was empirically informed from naturalistic observation of professional in-vehicle lessons (n = 110) and a survey of young adolescent drivers (n = 1627). The HOT-CAR model is presented as a three-component framework that recognises learning to drive occurs within a broader system beyond the Learner and the instructor. The foundation of the model identifies the nature of the relationship between the Learner and the instructor that underlies the success of all other elements. The core of the model is the higher-order driving instruction approach including teaching strategies informed by SDT’s needs-supportive model. The context for the model incorporates some of the immediate considerations relevant to instruction; for example, the graduated driver licensing system, automated vehicle features, and peer influence. An example of the application of the model is provided to reflect the immediate practicality of the HOT-CAR model to driver training. This contributes to the limited road safety literature providing a practical solution to Learner driver training that has potential to reduce the crash risk of young novice drivers. Importantly, the model has potential to be applied and adapted to education and other training environments where higher-order skills are a safety–critical component.

Modelling Ghanaian road crash risk using the Manchester driver behaviour Questionnaire

Little is known about how well models of driver crash risks from the developed world apply to motoring in African low and middle income countries. The Manchester Driver Behaviour Questionnaire (DBQ; Reason et al., 1990), which distinguishes deliberate violations of accepted safety practices and unintentional errors in its measurement of risky driving has been demonstrated to measure crash liability across a range of cultures. However, its applicability to Africa is currently unknown. The present study examines the psychometric properties of the DBQ in a sample of 453 Ghanaian drivers. A combination of exploratory and confirmatory factor analyses produced a 24 item 2-factor model of the DBQ with mainly violation items loading onto one factor and error items loading onto the other. Both violations and errors were independently correlated with self-reported crash involvement and violations correlated with self-reported traffic citations. Higher frequencies of violations and errors were reported by the Ghanaian sample than typically observed in the UK. The findings are consistent with the usefulness of the DBQ in characterising behaviours underlying crash risk in Ghana. The extent to which these risky behaviours may be targeted by behavioural, enforcement and engineering interventions is discussed.

Motion perception as a risk factor for motor vehicle collision involvement in drivers ≥ 70 years

To evaluate the relationship between visual function and a five-year history of motor vehicle collision rates in older adults. Motion perception impairment was explored as a risk factor for motor vehicle collisions for the first time in this study. Participants were licensed drivers ≥70 years old enrolled in the Alabama VIP Older Driver Study who underwent functional assessments for motion perception, distance visual acuity, contrast sensitivity, visual field sensitivity, and visual processing speed. Participants were recruited based on their being patients in an ophthalmology clinic in the year prior to enrollment or had participated in an earlier driving study. Crash reports were obtained from the Alabama Law Enforcement Agency for the 5 years prior to enrollment and mileage estimated using the Driving Habits Questionnaire. Crude and age-adjusted rate ratios (RRs) and 95 % confidence intervals (95 % CIs) were calculated using Poisson regression. 159 participants enrolled with a mean age of 79 years. The age-adjusted crash rate was higher among those with worse motion perception (RR: 2.7, 95 % CI: 1.4-5.2), severe slowing in visual processing speed (RR: 3.6, 95 % CI: 1.5-8.5), and impaired peripheral visual field sensitivity (RR: 2.4, 95 % CI: 1.3-4.4). Among a sample of older drivers, crash rates were higher for those with impaired motion perception, severely slowed visual processing speed, and impaired peripheral visual field sensitivity. The association between motion perception and crash risk in older drivers has not been previously reported. Prospective analysis of the Alabama VIP Older Driver Study will examine these risk factors for future collision involvement based on naturalistic driving data.

Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning

Driving style can substantially impact mobility, safety, energy consumption, and vehicle emissions. While a range of methods has been used in the past for driving style classification, the emergence of connected vehicles equipped with communication devices provides a new opportunity to classify driving style using high-resolution (10 Hz) microscopic real-world data. In this study, location-based big data and machine learning are used to classify driving styles ranging from aggressive to calm. This classification can be used to customize driver assistance systems, assess mobility, crash risk, fuel consumption, and emissions. This study’s main objective is to develop a framework that harnesses Basic Safety Messages (BSMs) generated by connected vehicles to quantify instantaneous driving behavior and classify driving styles in different spatial contexts using unsupervised machine learning methods. To this end, a subset of the Safety Pilot Model Deployment (SPMD) with more than 27 million BSM observations generated by more than 1300 individuals making trips on diverse roadways and through several neighborhoods in Ann Arbor, Michigan, were processed and analyzed. To quantify driving style, the concept of temporal driving volatility, as a surrogate safety measure of unsafe driving behavior, was utilized and applied to vehicle kinematics, i.e., observed speeds and longitudinal/lateral accelerations. Specifically, six volatility measures are extracted and used for classifying drivers. K-means and K-medoids methods are applied for grouping drivers in aggressive, normal, and calm clusters. Clustering results indicate that not only does driving style vary among drivers, but the thresholds for aggressive and calm driving vary across different roadway types due to variations in environment and road conditions. The proportion of aggressive driving styles was also higher on commercial streets than on highways and residential streets. Notably, we propose a Driving Score to measure driving performance consistently across drivers.

The Medical Referral Process and Motor-Vehicle Crash Risk for Drivers with Dementia.

Cognitive and physical impairment can occur with dementia and reduce driving ability. In the United States, individual states have procedures to refer and evaluate drivers who may no longer be fit to drive. The license review process is not well understood for drivers with dementia. This study uses comprehensive data from the Iowa Department of Transportation to compare the referral process for drivers with and without dementia from January 2014 through November 2019. The likelihood of failing an evaluation test was compared between drivers with and without dementia using logistic regression. The risk of motor-vehicle crash after referral for review of driving ability was compared using a Cox proportional hazard model. Analysis controlled for the age and sex of the referred driver. Drivers with dementia performed worse on all tests evaluated except the visual screening test. After the referral process, the risk of crash was similar between those with and without dementia. Drivers with dementia were denied their license more frequently than referred drivers without dementia. However, drivers with dementia who successfully kept their license as a result of the license review process were not at an increased risk of crash compared to other referred drivers.

Personality traits as predictors of cyclist behaviour

Road user behaviour and personality traits are important determinants of driver crash risk. While a great deal of research has been undertaken to understand the relationships between crash involvement, behaviours and personality traits for motor vehicle drivers, comparatively few studies have considered these factors for cyclists. This manuscript presents the findings of a study conducted amongst a sample of six hundred and fifteen (615) Australian cyclists, investigating these issues. The aim of this research was to establish a structure for a cycling behaviour questionnaire applicable to a cohort of Australian cyclists. Using the dimensions identified from the questionnaire, the research investigated the relationship between self-reported crashes, behaviours and personality traits, in order to further develop our understanding of risk factors associated with cycling. Personality traits (agreeableness, extroversion, conscientiousness, neuroticism and openness to experience) were measured using the Big Five Inventory. While cyclist behaviour was measured using a modified version of the cyclist behaviour questionnaire developed by the Dutch national road safety research centre (SWOV). Principal Components Analysis (PCA) was performed on the cycling behaviour questionnaire to identify underlying subscales of behaviour. The PCA identified a two dimension model representing violations (α = 0.74) and errors (α = 0.65), consisting of 16 items from the original 22 item cyclist behaviour questionnaire. Linear regressions for each of the cyclist behaviour factors identified that age was negatively associated with errors and violations, indicating that older cyclists report fewer errors or violations. Similarly, there was a negative association with average weekly kilometres travelled. Gender was a significant predictor of errors, but not violations, with male cyclists reporting fewer errors than females. When considering personality traits, there was a positive association between extroversion and both errors and violations. Significant negative associations were identified for agreeableness and conscientiousness. Neither neuroticism nor openness to experience were associated with the frequency of errors or violations. The research identified that demographics, travel characteristics and personality traits provide insight into engagement in aberrant cycling behaviours and these behaviours are associated with self-reported crash involvement. The research provides insight into behaviours that could be targeted with appropriate education and enforcement strategies.

Perceptions and attitudes toward risk and personal responsibility in the context of medical fitness to drive

Objective: Although there is a considerable body of literature probing the knowledge and awareness of doctors of guidelines on medical fitness to drive, little is known of knowledge and awareness of such guidelines among the general public. We investigated awareness, perceptions and attitudes among a range of adults toward risk and personal responsibility in the context of medical fitness to drive.Methods: This quasi-experimental between groups study assessed response differences between healthcare (33%) and non-healthcare professionals. Five hundred and fourteen adults (f = 342) completed a 15-item online survey, which assessed their license status, direct and secondhand experience with key medical conditions, perceptions of risk, driving intentions if advised to stop driving by a doctor and beliefs about responsibility for ensuring driver fitness.Results: Most of the participants had a driver license. Although the majority (87%) had no first-hand experience with key medical conditions contained in the Irish medical fitness to drive guidelines, two-thirds knew someone with at least one of these conditions. No participant admitted to an alcohol or drug misuse/dependence problem. Alcohol misuse/dependence and fatigue/chronic sleep loss were perceived as the greatest crash risk for drivers and for vulnerable road users. Risk perceptions in general public and the medical professionals were similar for most conditions but where they differed, the medical professionals perceived lower risk. Most respondents indicated that they would cease driving immediately if advised by their doctor for temporary and long-term conditions. No effects of age, gender, experience with medical conditions or professional status were noted on perceived intentions to cease driving. Perceptions about who is responsible for ensuring people are fit to drive fell into two categories. Drivers were perceived as chiefly responsible where alcohol, non-prescription drugs, fatigue and age were factors and doctors were identified as responsible in cases of diabetes, epilepsy, stroke, heart conditions and physical disabilities.Conclusions: These results suggest that more needs to be done to raise awareness of the risks posed by medical and other factors that reduce driver capability and also about drivers’ responsibility to ensure that they are fit to drive and thus improve road safety.

Medical Conditions and Crash Risk in Commercial Motor Vehicle Drivers.

The purpose of this study was to examine the association between diagnosed medical conditions and prospective crashes in commercial motor vehicle (CMV) drivers. Three databases (demographics, medical conditions, and crashes) from the Saskatchewan Government Insurance (SGI) were linked and filtered to examine whether various medical diagnoses were associated with prospective crashes from 2007 to 2017. Univariate and cox proportional hazard analysis were calculated for medical conditions and their association with crash risk. Crashes occurred on average within 2 years following a medical diagnosis. Between 16% and 21% of drivers with diabetes, vision impairment, sleep apnea and cardiovascular disease crashed post diagnosis. Licensing authorities and policy makers should consider further assessment if a CMV driver has been diagnosed with either diabetes or multiple sclerosis.