Although the safety performance of guardrail end terminals is tested using crash tests in the U.S., occupant injury risks are evaluated based on the flail-space model. This approach developed in the early 1980s neglects the influence of safety features (e.g. seatbelt, airbags, etc.) installed in late model vehicles. In this study, a vehicle (sedan, 1100 kg), a guardrail end terminal (ET-Plus) and a human body model (Global Human Body Model Consortium, GHBMC) were integrated to simulate car-to-end terminal crashes. Five velocities, two offsets, and two angles were used as pre-impact conditions. In all the 20 simulations, kinematics and kinetic data were recorded in GHBMC and vehicle models to calculate the GHBMC injury probabilities and vehicle-based injury metrics, correspondingly. Pre-impact velocity was observed to have the largest effect on the occupant injury measures. All the body-region and full-body injury risks increased with the increasing velocity. Meanwhile, the angles had a larger effect than offset to the change of full-body injury risk (9.1% vs. 0.3%). All the vehicle-based metrics had good correlations to full-body injury probabilities. Occupant Impact Velocity (OIVx), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) had a good correlation to chest, thigh, upper tibia, and lower tibia injuries. All the other correlations (e.g. brain/head injuries) were not statistically significant. The results pointed out that more vehicle-based metrics (ASI and THIV) could help improve the predictability in terms of occupant injury risks in the tests. Numerical methodology could be used to assess head and brain injury probabilities, which were not predictable by any vehicle-based metrics.

Paramedics face various unconventional and secondary task demands while driving ambulances, leading to significant cognitive load, especially during lights-and-sirens responses. Previous research suggests that high cognitive load negatively affects driving performance, increasing the risk of accidents, particularly for inexperienced drivers. The current study investigated the impact of anticipatory treatment planning on cognitive load during emergency driving, as assessed through the use of a driving simulator. We recruited 28 non-paramedic participants to complete a simulated baseline drive with no task and a cognitive load manipulation using the 1-back task. We also recruited 18 paramedicine students who completed a drive while considering two cases they were travelling to: cardiac arrest and infant seizure, representing varying difficulty in required treatment. The results indicated that both cases imposed considerable cognitive load, as indicated by NASA Task Load Index responses, comparable to the 1-back task and significantly higher than driving with no load. These findings suggest that contemplating cases and treatment plans may impact the safety of novice paramedics driving ambulances for emergency response. Further research should explore the influence of experience and the presence of a second individual in the vehicle to generalise to broader emergency response driving contexts.

There is a clear need to identify older drivers at increased crash risk, without additional burden on the individual or licensing system. Brief off-road screening tools have been used to identify unsafe drivers and drivers at risk of losing their license. The aim of the current study was to evaluate and compare driver screening tools in predicting prospective self-reported crashes and incidents over 24 months in drivers aged 60 years and older. 525 drivers aged 63-96 years participated in the prospective Driving Aging Safety and Health (DASH) study, completing an on-road driving assessment and seven off-road screening tools (Multi-D battery, Useful Field of View, 14-Item Road Law, Drive Safe, Drive Safe Intersection, Maze Test, Hazard Perception Test (HPT)), along with monthly self-report diaries on crashes and incidents over a 24-month period. Over the 24 months, 22% of older drivers reported at least one crash, while 42% reported at least one significant incident (e.g., near miss). As expected, passing the on-road driving assessment was associated with a 55% [IRR 0.45, 95% CI 0.29-0.71] reduction in self-reported crashes adjusting for exposure (crash rate), but was not associated with reduced rate of a significant incident. For the off-road screening tools, poorer performance on the Multi-D test battery was associated with a 22% [IRR 1.22, 95% CI 1.08-1.37] increase in crash rate over 24 months. Meanwhile, all other off-road screening tools were not predictive of rates of crashes or incidents reported prospectively. The finding that only the Multi-D battery was predictive of increased crash rate, highlights the importance of accounting for age-related changes in vision, sensorimotor skills and cognition, as well as driving exposure, in older drivers when using off-road screening tools to assess future crash risk.

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary \"Driving Classes\"; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.

Over 6.5 million commercial vehicle drivers were operating a large truck or bus in the United States in 2020. This career often has high stress and long working hours, with few opportunities for physical activity. Previous research has linked these factors to adverse health conditions. Adverse health conditions affect not only the professional drivers\' wellbeing but potentially also commercial motor vehicle (CMV) operators\' safe driving ability and public safety for others sharing the roadway. The prevalence of health conditions with high impact on roadway safety in North American CMV drivers necessitates empirical epidemiological research to better understand and improve driver health. The paper presents four challenges in conducting epidemiological research with truck and bus drivers in North America and potential resolutions identified in past and current research. These challenges include (1) the correlation between driving performance, driving experience, and driver demographic factors; (2) the impact of medical treatment status on the relationship between health conditions and driver risk; (3) capturing accurate data in self-report data collection methods; and (4) reaching the CMV population for research. These challenges are common and influential in epidemiological research of this population, as drivers face severe health issues, health-related federal regulations, and the impact of vehicle operation on the safety of themselves and others using the roadways.

BACKGROUND: Truck driving is one of the occupations with high injury rates.
OBJECTIVE: This study investigates the relationships between age, work experience, workdays, monthly income, perceived job risk, satisfaction of working conditions, safety awareness, and job satisfaction of truck drivers. Also, this study analyzes the effects of satisfaction of working conditions and safety awareness on the job satisfaction of truck drivers.
METHODS: This study interviewed 278 truck drivers and surveyed age, work experience, workdays, monthly income, perceived job risk, satisfaction of working conditions, safety awareness, and job satisfaction. A regression analysis was performed to determine leading factors affecting safety and satisfaction and the relationships.
RESULTS: The results showed that the number of workdays was related to monthly income, perceived job risk, and job satisfaction. The monthly income of truck drivers was increased according to workdays and age. Perceived job risk increased with number of days worked. Safety awareness decreased with the monthly income, and job satisfaction decreased with perceived job risk level, workdays, and work experience. Finally, job satisfaction was directly affected by satisfaction with working conditions and indirectly affected by safety awareness.
CONCLUSIONS: The results suggest that an increase in satisfaction of working conditions can enhance safety awareness and job satisfaction.

Motor vehicle crashes are one of the leading causes of death among teenagers. Many of these deaths are due to preventable causes, including impaired and distracted driving. You Drink, You Drive, You Lose (YDYDYL) is a prevention program to educate high school students about the consequences of impaired and distracted driving. YDYDYL was conducted at a public high school in Southern Nevada in March 2020. A secondary data analysis was conducted to compare knowledge and attitudes of previous participants with first-time participants. Independent-samples-t test and χ2 test/Fisher’s exact test with post-contingency analysis were used to compare pre-event responses between students who had attended the program one year prior and students who had not. Significance was set at p < 0.05. A total of 349 students participated in the survey and were included for analysis; 177 had attended the program previously (50.7%) and 172 had not (49.3%). The mean age of previous participants and first-time participants was 16.2 (SD ± 1.06 years) and 14.9 (SD ± 0.92 years), respectively. Statistically significant differences in several self-reported baseline behaviors and attitudinal responses were found between the two groups; for example, 47.4% of previous participants compared to 29.4% of first-time participants disagreed that reading text messages only at a stop light was acceptable. Students were also asked how likely they were to intervene if a friend or family member was practicing unsafe driving behaviors; responses were similar between the two groups. The baseline behaviors and attitudes of participants regarding impaired and distracted driving were more protective among previous participants compared to first-time participants, suggesting the program results in long-term positive changes in behaviors and attitudes. The results of this secondary retrospective study may be useful for informing the implementation of future impaired and distracted driving prevention programs.

From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car drivers must keep a safe driving dynamic, having an unaltered physiological status while processing the surrounding information coming from the driving scenario (e.g., traffic signs, other vehicles and pedestrians). Specifically, the identification and tracking of pedestrians in the driving scene is a widely investigated problem in the scientific community. The authors propose a full, deep pipeline for the identification, monitoring and tracking of the salient pedestrians, combined with an intelligent electronic alcohol sensing system to properly assess the physiological status of the driver. More in detail, the authors propose an intelligent sensing system that makes a common air quality sensor selective to alcohol. A downstream Deep 1D Temporal Residual Convolutional Neural Network architecture will be able to learn specific embedded alcohol-dynamic features in the collected sensing data coming from the GHT25S air-quality sensor of STMicroelectronics. A parallel deep attention-augmented architecture identifies and tracks the salient pedestrians in the driving scenario. A risk assessment system evaluates the sobriety of the driver in case of the presence of salient pedestrians in the driving scene. The collected preliminary results confirmed the effectiveness of the proposed approach.

Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabetes mellitus (DM). We applied these methodologies over 4 weeks of continuous observation to quantify differences in real-world driver behavior profiles associated with physiologic changes in drivers with DM (N=19) and without DM (N=14). Results showed that DM driver behavior changed as a function of glycemic state, particularly hypoglycemia. DM drivers often drive during at-risk physiologic states, possibly due to unawareness of impairment, which in turn may relate to blunted physiologic responses (measurable heart rate) to hypoglycemia after repeated episodes of hypoglycemia. We found that this DM driver cohort has an elevated risk of crashes and citations, which our results suggest is linked to the DM driver\'s own momentary physiology. Overall, our findings demonstrate a clear link between at-risk driver physiology and real-world driving. By discovering key relationships between naturalistic driving and parameters of contemporaneous physiologic changes, like glucose control, this study directly advances the goal of driver-state detection through wearable physiologic sensors as well as efforts to develop \"gold standard\" metrics of driver safety and an individualized approach to driver health and wellness.

BACKGROUND: Older drivers have a crash rate nearly equal to that of young drivers whose crash rate is the highest among all age groups. Contrast sensitivity impairment is common in older adults. The purpose of this study is to examine whether parameters from the photopic and mesopic contrast sensitivity functions (CSF) are associated with incident motor vehicle crash involvement by older drivers.
METHODS: This study utilized data from older drivers (ages ≥60 years) who participated in the Strategic Highway Research Program Naturalistic Driving Study, a prospective, population-based study. At baseline participants underwent photopic and mesopic contrast sensitivity testing for targets from 1.5-18 cycles per degree. Model fitting generated area under the log CSF (AULCSF) and peak log sensitivity. Participant vehicles were instrumented with sensors that captured continuous driving data when the vehicle was operating (accelerometers, global positioning system, forward radar, 4-channel video). They participated for 1-2 years. Crashes were coded from the video and other data streams by trained analysts.
RESULTS: The photopic analysis was based on 844 drivers, and the mesopic on 854 drivers. Photopic AULCSF and peak log contrast sensitivity were not associated with crash rate, whether defined as all crashes or at-fault crashes only (all p > 0.05). Mesopic AULCSF and peak log sensitivity were associated with an increased crash rate when considered for all crashes (rate ratio (RR): 1.36, 95% CI: 1.06-1.72; RR: 1.28, 95% CI: 1.01-1.63, respectively) and at-fault crashes only (RR: 1.50, 95% CI: 1.16-1.93; RR: 1.38, 95% CI: 1.07-1.78, respectively).
CONCLUSIONS: Results suggest that photopic contrast sensitivity testing may not help us understand future crash risk at the older-driver population level. Results highlight a previously unappreciated association between older adults\' mesopic contrast sensitivity deficits and crash involvement regardless of the time of day. Given the wide variability of light levels encountered in both day and night driving, mesopic vision tests, with their reliance on both cone and rod vision, may be a more comprehensive assessment of the visual system\'s ability to process the roadway environment.