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New ministerial decree restricts driving motorized vehicles for patients with Alzheimer's disease and related disorders. Reisberg stage 3, threshold used to contraindicate driving, appears to correspond to a mild stage of major neurocognitive impairment. A single scale gives an idea of the level of risk but does not provide a holistic assessment. The aim of this consensus is to put forward recommendations from several French learned societies for individualized cognitive assessments to minimize the risks associated with driving and its cessation. Fitness to drive should be raised at the earliest stages of the diagnostic process, and regularly throughout the follow-up. Consult a registered doctor is recommended to all patients wishing to continue driving. All documents must be given to the patient only. An alternative must always be offered to patients who are recommended a modal shift.

Public safety (prevention of accidents) is the primary objective in assessing fitness to drive a motor vehicle. However, general access to mobility should not be restricted if there is no particular risk to public safety. For people with diabetes mellitus, the Führerscheingesetz (Driving Licence Legislation) and the Führerscheingesetz-Gesundheitsverordnung (Driving Licence Legislation Health enactment) regulate important aspects of driving safety in connection with acute and chronic complications of the disease. Critical complications that may be relevant to road safety include severe hypoglycemia, pronounced hyperglycemia and hypoglycemia perception disorder as well as severe retinopathy and neuropathy, endstage renal disease and certain cardiovascular manifestations. If there is a suspicion of the presence of one of these complications, a detailed evaluation is required.In addition, the individual antihyperglycemic medication should be checked for existing potential for hypoglycemia. Sulfonylureas, glinides and insulin belong to this group and are therefore associated with the requirement of a 5-year limitation of the driver's license. Other antihyperglycemic drugs without potential for hypoglycemia such as Metformin, SGLT‑2 inhibitors (Sodium-dependent-glucose-transporter‑2 inhibitors, gliflozins), DPP-4-inhibitors (Dipeptidyl-Peptidase inhibitors, gliptins), and GLP‑1 analogues (GLP‑1 rezeptor agonists) are not associated with such a time limitation.The relevant laws which regulate driving safety give room for interpretation, so that specific topics on driving safety for people with diabetes mellitus are elaborated from a medical and traffic-relevant point of view. This position paper is intended to support people involved in this challenging matter., (© 2023. The Author(s).)

We exploit a reduction in the minimum legal drinking age (MLDA) in New Zealand from 20 to 18 to study the dynamics of youth risk-taking. Using the universe of road accidents over 15 years and an event history approach, we find no evidence that lowering the drinking age increased alcohol-related accidents among teens. Complementary results of a cohort analysis suggest that reducing the drinking age even led to a short-term decline in risky driving among youths directly affected by the MLDA change but had no longer-run impacts on youth risky driving and drinking behaviors., (© 2024 The Author(s). Health Economics published by John Wiley & Sons Ltd.)

Introduction: Previously, we identified eight effective consultation skills to support decision-making in the voluntary surrender of older adult drivers' licences in super-aged Japan. This study aimed to clarify the transferability of these skills., Methods: We collected text data by interviewing 11 safe-driving counsellors (four police officers, four clerical staff and three nurses) in the License Division of the National Police Agency from February to March 2022. Interviews were semi-structured and conducted by telephone or email. During the interview, participants were asked to recall their experiences as counsellors providing decision-making support to older drivers and to compare their experiences with the eight consultation skills. We analysed the content of the responses by quantitative text analysis with KH Coder 3 software., Results: As a characteristic of the words and phrases used by counsellors in their narratives about consultation skills, the most frequently extracted words from among 3147 words were think, parties and family, and promote had the highest mediation centrality. The eight subgraphs were 'Respecting the will of relevant parties from their standpoint is natural', 'Listening attentively and empathetically to relevant parties is effective', 'Presenting objective data to guide decisions is successful', 'Showing cognitive functioning test results is often effective', 'Counselors with medical expertise can elicit positive counseling outcomes', 'Intervention by medical or police counselors facilitates the decision to surrender voluntarily', 'Counseling skills need to be improved' and 'A diagram of the 8 skills is helpful for inexperienced counselors'., Conclusion: The results suggest that the eight consultation skills have similarities and are transferable. This transferability might contribute to practical application or cohort follow-up study research. These skills can be incorporated into counsellor training, and counsellors can be expected to use these skills in the future. Regardless of the safe-driving counsellor's years of experience, the skills can help them provide uniform and accurate support in decision-making regarding the voluntary surrender of older adult drivers' licences. These skills are a promising approach to help older adults lead safe and secure lives as they age., (© 2024 John Wiley & Sons Ltd.)

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have a high level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be a priority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in a critical situation., (© 2024. Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature.)

Significance: Autonomous vehicles (AVs) have the promise to be an alternative transportation solution for those with vision loss. However, the impact of vision loss on the perceptions and concerns of AVs is unknown. This study therefore examined whether AVs are perceived differently by blind, visually impaired (VI), and normally sighted people., Purpose: This study compared the perceptions of AVs among the blind, VI, and normally sighted., Methods: Participants' opinions on four perception measures (general opinion, trust, impact on quality of life, and intention to use AVs) and nine concerns regarding AVs were measured. The survey was administered to 51 normally sighted, 68 VI, and 65 blind participants. Analyses of covariance assessed whether the four perception measures and nine concerns varied by vision status (normal vision, VI, blind) and driving status (driver, nondriver). Univariate correlations and multiple regression analyses identified associations and predictors of AV perceptions and concerns from demographic, mood, cognition, travel behavior, and vision measures, which included visual acuity, contrast sensitivity, and visual field., Results: The blind (p<0.001), VI (p<0.001), and nondrivers (p<0.001) showed a greater intention to use AVs compared with those with normal vision and drivers. Similar findings were found for the other perception measures. As visual acuity, contrast sensitivity, and visual field extent declined, positivity toward AVs increased (p<0.001). Visual field extent best predicted general opinion and trust in AVs, whereas driving measures were the best predictors of impact on quality of life and intention to use AVs. Concerns about AVs showed no differences based on vision (p=0.94) or driving (p=0.63) status., Conclusions: Individuals with vision loss expressed more acceptance of AVs despite their concerns. How positive someone is toward AVs appears to be dependent on their visual field extent and driving status., Competing Interests: Conflict of Interest Disclosure: None of the authors have reported a financial conflict of interest., (Copyright © 2024 American Academy of Optometry.)

Background: With the deterioration of cognitive functions, the capability to obtain information with speed, one of the essential elements needed to perform safe driving, may be impacted. We aimed to compare the legibility of analogue and digital speedometers for senior drivers with cognitive impairment, and examined the demographic, cognitive, and driving-related variables that predict their speedometer-reading performance., Methods: A total of 50 senior drivers with cognitive impairment were investigated and asked to complete an office-based speedometer-reading test using an iPad. Two general types of speedometers (analogue and digital) were tested in this study., Results: The age of the participants ranged from 61 to 92 years (mean (SD), 79.10 (6.973)), and 29 were male. The mean (SD) score of the Mini-Mental State Examination was 22.48 (6.089). The median (Q L , Q U ) scores of the analogue and digital speedometer-reading tests were 4 (4, 5.25) and 6 (6), respectively. Based on the result of the Wilcoxon signed-rank test, the score of the digital speedometer-reading test was significantly higher than that of the analogue one (Z = 4.399, P < 0.001). The results of multiple linear regression analyses show that the scores of the Mini-Mental State Examination (β = 0.358, P = 0.025), and the trail-making test-A (β = -0.443, P = 0.006) predicted the digital speedometer-reading performance, and they together explain 54.7% of the total variance., Conclusions: A digital speedometer was found to be easier for absolute value reading for senior drivers with cognitive impairment, compared to an analogue speedometer. Senior drivers with subjective cognitive decline may also have impairments in obtaining the speed information through an analogue speedometer. General cognitive function and attention may influence the speed-reading performance on the digital speedometer., (© 2022 Japanese Psychogeriatric Society.)

Human-automation cooperation has become ubiquitous. In this concept, automation refers to autonomous machines, robots, artificial intelligence, and other autonomous nonhuman agents. A human driver will share control of semiautonomous vehicles (semi-AVs) with an automated system and thus share responsibility for crashes caused by semi-AVs. Research has not clarified whether and why people would attribute different levels of blame and responsibility to automation (and its creators) and its human counterpart when each causes an equivalent crash. We conducted four experiments in two studies (total N = 1,045) to measure different responses (e.g., severity and acceptability judgment, blame and responsibility attribution, compensation judgment) to hypothetical crashes that are caused by the human or the automation in semi-AVs. The results provided previously unidentified evidence of a bias, which we called the "blame attribution asymmetry," a tendency that people will judge the automation-caused crash more harshly, ascribe more blame and responsibility to automation and its creators, and think the victim in this crash should be compensated more. This asymmetry arises in part because of the higher negative affect triggered by the automation-caused crash. This bias has a direct policy implication: a policy allowing "not-safe enough" semi-AVs on roads could backfire, because these AVs will lead to many traffic crashes, which might in turn produce greater psychological costs and deter more people from adopting them. Other theoretical and policy implications of our findings were also discussed., (© 2021 Society for Risk Analysis.)

The primary aim of this systematic review was to examine the efficacy of driving interventions with regard to a reduction in motor vehicle crashes and improvements in driving skills among older people. The secondary aim was to identify the optimal type (on-road or off-road) and dosage (period, sessions, and duration) of driving interventions for improving driving skills in older people. We searched MEDLINE, EMBASE, PsycINFO, and Scopus of Systematic Reviews for papers published from their inception to December 1, 2020, as well as the reference lists of the included papers. The selected studies were randomized controlled trials examining the effects of driving interventions among community-dwelling older drivers aged 65 years and over. A meta-analysis of two studies (n = 960) showed that driving interventions significantly reduced the number of motor vehicle crashes per person-years. Ten studies (n = 575) were included in the meta-analysis showing that the interventions significantly improved the driving skill scores. Driving skill scores significantly improved after on-road training, and in interventions of at least 3 h, 3 sessions, and 3 weeks. Driving interventions significantly improve driving skills and reduce motor vehicle crashes among older drivers aged 65 years and over. On-road training is more efficacious than off-road training and driving interventions of at least 3 h taking place in 3 sessions over a period of 3 weeks may be required to improve driving skills in older drivers. Geriatr Gerontol Int 2023; 23: 771-778., (© 2023 The Authors. Geriatrics & Gerontology International published by John Wiley & Sons Australia, Ltd on behalf of Japan Geriatrics Society.)

The driving pattern of three-wheeled auto-rickshaws is governed by commuter's demands on certain fixed roads that offer flexible mobility solutions in a mid-size city. This flexibility creates unique driving patterns, frequent stop-and-go conditions, frequent acceleration, braking, and excessive idling, which affect emission rates. Existing emission testing regulation based on the driving cycle does not represent real-world conditions. In this paper, the real-world driving cycle of three-wheeled auto-rickshaw has been developed to provide realistic CO, HC, and NO X pollutants and see the effect of introducing modal shift of electric auto-rickshaw to reduce emission for India. Two policy scenarios were evaluated (1) with a 5% modal shift to electric auto-rickshaw, and (2) without modal shift. The results indicate that with a 5% shift to electric auto-rickshaw, by 2030, emissions will decrease by 6.30% compared to the baseline scenario. Further, by 2030, the projected CO emission would be 1,696,670 ton/year, and HC and NO X emissions would be 2,067,371 ton/year. Results can be useful for policy interventions towards cleaner fuel and the aggressive adoption for reducing pollution from auto-rickshaw., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The graduated driver licensing (GDL) program requires teenage drivers to pass through an intermediate stage, which contains specific driving restrictions such as a night curfew or a limit on the number of teen passengers to be carried, before earning full driving privileges. Using individual data from the 1999 to 2017 biennial Youth Risk Behavior Surveillance System (YRBS) combined with state-level GDL policy variations, we estimate the effects of GDL on teenage body weight in the United States. We find that the presence of GDL raises adolescents' body mass index Z-score and their likelihood of being overweight or obese. Among the restrictions imposed, a night curfew implemented together with a passenger restriction makes the most significant impact. These estimated effects are concentrated among states with more restrictive GDL policies. We also find that the presence of GDL reduces adolescent physical activity and heavy smoking, while increasing their time spent watching TV and milk intake, perhaps contributing to youth weight gain. An event study analysis reveals that the effects of GDL on adolescent weight increase may be transitory., (© 2021 John Wiley & Sons Ltd.)

Background: Metabolic syndrome (MetS) is especially prevalent among US truck drivers. However, there has been limited research exploring associations between MetS conditions with roadway crashes among truck drivers. The objective of this paper is to assess relationships between specific combinations of individual MetS components and crashes and near-misses., Methods: Survey, biometric, and anthropometric data were collected from 817 truck drivers across 6 diverse US states. Survey data focused on demographics and roadway safety outcomes, and anthropometric/biometric data corresponded to five MetS conditions (waist circumference blood pressure, hemoglobin A1c, triglycerides, and high-density lipoprotein [HDL] cholesterol). Logistic regression was used to calculate odds ratios of lifetime crashes and near-miss 1-month period prevalence associated with: 1) specific MetS conditions regardless of presence or absence of other MetS conditions, and 2) specific MetS conditions and counts of other accompanying MetS conditions., Results: Hypertension was the MetS characteristic most strongly associated with lifetime crash and 1-month near-miss outcomes, while high triglycerides, low HDL cholesterol, and large waist circumference were most commonly present among groups of conditions associated with crashes and near-misses. Overall, an increasing number of specific co-occurring MetS conditions were associated with higher reporting of roadway crashes., Conclusions: Specific combinations and higher prevalence of MetS conditions were associated with increased frequency of reported crashes. Moreover, when the co-occurrence of MetS conditions is aggregated, a dose-response relationship with crashes appears. These results suggest that policy changes and interventions addressing MetS may increase driver health and reduce crash risk., (© 2022 Wiley Periodicals LLC.)

Purpose: Optimal cognitive performance might prevent vehicle accidents. Identifying time-related circadian and homeostatic parameters having an impact on cognitive performance of drivers may be crucial to optimize drivers' performance., Methods: In this prospective study conducted on bus drivers, two drivers alternated driving during a 24-h round trip and were accompanied by an interviewer. Each driver was tested using Karolinska Sleepiness Scale (KSS) and the reversed digit span Wechsler Working Memory test before the start of his shift and then every 6 h during a "work/driving" day. Psychomotor Vigilance Task (PVT) was assessed before and after the journey. Linear mixed model was used to explore the factors affecting cognitive performance and sleepiness in univariate and multivariate analysis., Results: Among 35 bus drivers, the effect of time of day on working memories was statistically significant (p = 0.001), with the lowest working memory scores at 04:00 am (± 1). The highest score of subjective sleepiness was also at 04:00 am (± 1). The time on task parameter affected sleepiness significantly (p = 0.024) and sleepiness was significantly associated with decreased working memory. Psychomotor Vigilance Task reaction time mean and the number of minor lapses were significantly increased after the journey, which suggested decreased vigilance. In multivariable analysis, a longer interval between the beginning of working hours and testing time (B (95% CI) = 15.25 (0.49 to 30), p = 0.043) was associated with higher (i.e., slower) PVT reaction time mean., Conclusions: These results suggest that optimizing bus drivers' working schedules may improve drivers' sleepiness and cognitive performance and thus increase road safety., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Driving simulators are an increasingly important tool to develop vehicle functionalities and to study driver or passenger responses. A major hindrance to the use and validity of such studies is Simulator Sickness (SS). Several studies have suggested a positive relation between improvements in simulator fidelity and the likelihood of sickness. We hypothesized that this relation only holds true for static (fixed-base) simulators, and that increased fidelity in fact reduces simulator sickness in dynamic (moving-base) simulators. We performed a meta-analysis investigating the relation between sickness and fidelity in static and dynamic systems. A literature search yielded a total of 41 simulator studies that varied aspects of mechanical and/or visual fidelity and assessed SS for the same driving conditions and the same or equivalent participant groups. Evaluation of a model synthesizing the findings of these studies indicates that SS decreases with visual fidelity, and suggests that this effect may be negated for static simulators. The results of the modeling efforts thereby provide some support for the hypothesis that increased fidelity can reduce SS in dynamic simulators. Based on the evaluation of the literature we also note particular shortcomings and gaps in available research. Finally, we make recommendations for specific experiments that may fill these gaps and allow definitive conclusions on the role of simulator fidelity in SS., (© 2022. The Author(s).)

This article evaluates the driver's reaction times in a driving simulator environment. The research focused mainly on young drivers under the age of 26, who cause many accidents. Each participating driver provided basic information later used for mathematical-statistical analysis. The main advantage of driving simulators is limitless usage. It is possible to simulate situations that would be unacceptable in real road traffic. Therefore, this study is also able to examine drunk driving. The main goal of the article is to evaluate if gender, practice, or alcohol significantly affected the reaction time of 30 drivers. We also focused on drinking before driving for a smaller number of the drivers; ten of them performed driving under the influence of alcohol. For these mathematical-statistical purposes, we used a one-sample t -test, a paired-samples t -test, an independent-sample t -test, and a correlation analysis together with the assessment of its statistical significance.

It is challenging to determine which patients with obstructive sleep apnea (OSA) have impaired driving ability. Vulnerability to this neurobehavioral impairment may be explained by lower brain metabolites levels involved in mitochondrial metabolism. This study compared markers of brain energy metabolism in OSA patients identified as vulnerable vs resistant to driving impairment following extended wakefulness. 44 patients with moderate-severe OSA underwent 28hr extended wakefulness with three 90min driving simulation assessments. Using a two-step cluster analysis, objective driving data (steering deviation and crashes) from the 2nd driving assessment (22.5 h awake) was used to categorise patients into vulnerable (poor driving, n = 21) or resistant groups (good driving, n = 23). 1 H magnetic resonance spectra were acquired at baseline using two scan sequences (short echo PRESS and longer echo-time asymmetric PRESS), focusing on key metabolites, creatine, glutamate, N-acetylaspartate (NAA) in the hippocampus, anterior cingulate cortex and left orbito-frontal cortex. Based on cluster analysis, the vulnerable group had impaired driving performance compared with the resistant group and had lower levels of creatine (PRESS p = ns, APRESS p = 0.039), glutamate, (PRESS p < 0.01, APRESS p < 0.01), NAA (PRESS p = 0.038, APRESS p = 0.035) exclusively in the left orbito-frontal cortex. Adjusted analysis, higher glutamate was associated with a 21% (PRESS) and 36% (APRESS) reduced risk of vulnerable classification. Brain mitochondrial bioenergetics in the frontal brain regions are impaired in OSA patients who are vulnerable to driving impairment following sleep loss. These findings provide a potential way to identify at risk OSA phenotype when assessing fitness to drive, but this requires confirmation in larger future studies., (© 2021 European Sleep Research Society.)

Train drivers work long hours on 24 h schedules and many factors impact their fatigue risk at work, creating a clear imperative for good rostering practice. Adopting a systems approach, this study investigated the relationship between multiple interrelated factors (train drivers' schedule, sleep, wellbeing, and fatigue) and the perceived influence of these factors on train driving performance and safety using an online survey distributed in Australia and New Zealand. In addition to demographics and work schedule, passenger and freight train drivers (n = 751) answered questions about: (1) Sleep duration; (2) wellbeing, including physical and mental health, the extent to which shiftwork causes sleep, social, domestic, and work problems, and (3) the extent to which ten fatigue, health and wellbeing factors in the work and home environment negatively impact their driving performance. The key factor that emerged from analyses, with the largest and most consistent negative effects (and controlling for other factors) was schedule irregularity. Approved rosters were ranked as having the most important impact on day-to-day driving performance, followed by physical and mental health, and outside work factors. Results also suggested that schedule irregularity may amplify the negative impacts of the roster, impaired physical and mental health, and outside work factors on driving performance. As shift variability and schedule irregularity are often poorly represented in existing industry guidance, these results provide evidence for increased reflection on current fatigue management guidelines for train drivers and suggest a need for greater focus on schedule irregularity through the lens of a systems approach., (© 2022. The Author(s).)

Rationale: The effects of hypnotics on automobile driving have been attracting increasing attention. However, few driving simulators (DSs) have been confirmed to have acceptable reliability and validity for assessing the next-day residual effects of zopiclone as a positive control on driving performance., Objective: To investigate whether a new DS could permit detection of the next-day residual effects of zopiclone on driving performance., Methods: In this double-blind, randomized, placebo-controlled crossover trial, 28 healthy males received zopiclone 7.5 mg at bedtime on days 1 and 8 and placebo on the other days over a period of 16 days. The participants took part in three driving tasks-road-tracking, car-following, and harsh-braking-using a DS on days 2 and 9 at 9-h post-dosing. Scores on the Karolinska Sleepiness Scale and Profile of Mood States-Second Edition were then assessed, as was the serum concentration of zopiclone., Results: The estimated differences in the standard deviation of lateral position (cm) in the road-tracking task between the zopiclone and placebo groups on days 2 and 9 were 3.75 cm (90% confidence interval (CI): 1.71-5.79) and 4.07 cm (90% CI: 2.02-6.11), respectively. The estimated differences in the distance coefficient of variation in the car-following task and in the brake reaction time in the harsh-braking task between the zopiclone and placebo groups on day 2 were 4.31 (90% CI: 1.94-6.69) and 24.6 ms (90% CI: 12.7-36.4), respectively., Conclusions: The DS used in this study has sufficient sensitivity to detect the next-day residual effects of zopiclone on driving performance., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: First-generation antihistamines are considered driving-impairing medicines (DIM), while second- and third-generation antihistamines are relatively safe for driving., Objectives: The aim of this study was to know the trend of consumption of antihistamines and other DIMs in Spain between 2015 and 2019., Methods: This is a population-based registry study. The population distribution by age and gender has been taken into account, as well the treatment duration with these medicines and the concomitant use of other DIMs. Adjusted consumption for licensed drivers is also presented., Results: Between 2015 and 2019, antihistamines were dispensed to 12.1% of the population and 9.25% of drivers. Oral antihistamines are the most consumed with 85.83%, and generally more used by women than men. Regardless of systemic antihistamines, the second-generation were the most consumed (8.9%) followed by the third-generation (2.07%) and the first-generation (0.61%). Subacute use was predominant in second -generation antihistamines (4.96%) and third-generation (1.26%), while acute use was predominant in third-generation antihistamines. On the other hand, only 0.36% of the population consumed antihistamines daily. The concomitant use of antihistamines with other DIMS was considerable, especially anxiolytics, opioids, other analgesics and antipyretics and antidepressants. The results in drivers were similar than in the general population., Conclusions: The use of antihistamines has increased in recent years, however, in Spain, the use of less sedatives predominates, which is safe for driving. Finally, it is important to consider that concomitant use with other DIMs was frequent, which may affect the fitness to drive., (© 2021 Société Française de Pharmacologie et de Thérapeutique.)

The purpose of this multi-site randomised controlled trial was to evaluate the effectiveness of a Driving Training Program, an intervention designed for student drivers with autism spectrum disorder (ASD). Participants were 72 student drivers with ASD (ages 16-31) who were randomly assigned to an intervention or control group. Student drivers received ten driving lessons with a professional driving instructor via a standardised driving route. The Driving Performance Checklist was used as the outcome measure to evaluate the driving performance of student drivers during on-road pre- and post-observational drives. Both groups showed an improvement in driving performance, however, the extent of improvement between groups was not significant. Findings showed promising intervention efficacy for training student drivers with ASD to drive., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.)

Introduction: Driving is a valued occupation given the independence and freedom it provides. Safe driving performance can be impacted by medical conditions, change in functional status and ageing processes. Occupational therapy driver assessors (OTDAs) provide invaluable driving recommendations; however, this requires specialist training for the therapist and is costly for clients. The number of OTDAs is not expected to meet the growing demand for expert services in this area, and little is known about the practices that non- OTDAs use to assist clients with returning to driving. The aims of this study were to investigate the practices of non-OTDAs in a community-based rehabilitation setting in Australia with respect to knowledge, confidence and skills in assessments, recommendations and outcomes for clients as part of the return to driving process., Methods: A descriptive study including medical record audits between April and September 2019 and staff surveys were completed at a large metropolitan community-based rehabilitation facility. Descriptive statistics and thematic analysis were used to summarise data., Results: A total of 102 client medical records were audited, and 13 clinician surveys were completed. Medical record audits identified that return to driving was not consistently addressed by occupational therapists. Clinician surveys outlined a lack of knowledge and confidence of return to driving processes and available assessment tools to guide this process., Conclusion: All occupational therapists have an ethical obligation to address driving as an activity of daily living; however, non-OTDAs report that they are not equipped for this role. This may negatively impact on driver safety, independence and overall health and well-being of clients in community-based rehabilitation. Further research is indicated to develop evidence-based driving resources to support best practice of non-OTDAs., (© 2021 Occupational Therapy Australia.)

In recent years, smart cars have advanced rapidly and use artificial intelligence technology to predict behavior, make decisions, and control goals. This technology significantly determines the knowledge level of vehicles. In the complex and dynamic environment of road traffic, negligence, and inattention can lead to irreparable damage. Real-time identification and positioning of road lines are key to improving the safety of driving cars. To improve the performance of safe driving assistance, this paper shows how to detect road lines using the YOLOV8 algorithm and then make decisions to continue driving straight, turn right, and turn left. Simulation results and accuracy comparison show that this approach can be used as a reliable source for creating driving assistance scenarios in natural road traffic environments. The use of artificial intelligence and the precise architecture of YOLOV8 promise high speed and accuracy in smart cars. [ABSTRACT FROM AUTHOR]

The validity of driving simulators is a topic that has been addressed ever since their first use. In practice, a comparison of field data and simulator data is often used for validation purposes. A systematic literature search was conducted which identified large heterogeneity in both study designs and statistical methods used in research on simulator validity. Most of the included studies concluded simulator validity (thus equivalence of field data and simulator data) from non-significant results in Null-Hypothesis Significance Testing (NHST), which is a well-known yet somewhat controversial practice. In this work, the consequences of both study design choice and the choice of the statistical method are outlined and alternatives such as using Bayes factor hypothesis testing and equivalence tests are discussed and applied to a real data example. The present paper is intended to shed light on the influence of the statistical method on conclusions drawn from experimental data. For research on driving simulator validity, we highly encourage the use of equivalence tests or Bayes factor hypothesis tests, in particular when evidence of the data suggests that an effect might be absent. [ABSTRACT FROM AUTHOR]

Road detection technology is an important part of the automatic driving environment perception system. With the development of technology, the situations that automatic driving needs to consider will become broader and more complex. This paper contributes a lightweight convolutional neural network model, incorporating novel convolution and parallel pooling modules, an improved network activation function, and comprehensive training and verification with multiple datasets. The proposed model achieves high accuracy in detecting drivable areas in complex autonomous driving situations while significantly improving real-time performance. In addition, we collect data in the field and create small datasets as reference datasets for testing algorithms. This paper designs relevant experimental scenarios based on the datasets and experimental platforms and conducts simulations and real-world vehicle experiments to verify the effectiveness and stability of the algorithm models and technical solutions. The method achieves an MIoU of 90.19 and a single batch time of 340 ms with a batch size of 8, which substantially reduces the runtime relative to a typical deep network structure like ResNet50. [ABSTRACT FROM AUTHOR]

It is very important for autonomous driving vehicles to accurately detect objects such as vehicles and pedestrians on the road in real time. Aiming at the problems of missed detection and poor detection effect of small targets in the autonomous driving scene, this paper proposes an automatic driving target detection algorithm that improves the YOLOv7 algorithm. Firstly, it modifies the modules in the network to expand the receptive field, reduces the size of the receptive field module, and improves the speed of the model and enhances the ability to extract image feature information. Secondly, the paper introduces the BRA attention mechanism at the output of the backbone network to improve the model's ability to small target objects. Finally, it replaces the original CIOU loss function of the algorithm with the EIOU loss function to minimize the difference between the height and width of the predicted frame and the real frame, and speeds up the convergence of the model while achieving better positioning results. The experimental results show that: on the KITTI dataset, when the improved YOLOv7 algorithm performs target detection, its mAP reaches 94.7%, which is 3.1 percentage points higher than the original YOLOv7 algorithm, and it has achieved higher detection accuracy in small target object detection. It effectively improves the model's detection effect on small targets. [ABSTRACT FROM AUTHOR]

Park green space is a big part of public infrastructure in cities, and how to evaluate and optimize the mismatch of urban park green space (UPGS) has become the focus of current research in academia and industry. Taking China's 286 cities as an example, this paper used the spatial cluster and Boston Consulting Group Matrix to analyze the aggregation laws and changing modes of UPGS from 2010 to 2020, introduced the spatial mismatch model to analyze the matching of its supply and demand with GDP and population, and adopted the Geodetector to analyze the influencing factors. The findings: (1) The evolution of UPGS in China had long been characterized by a "pyramidal" pattern, i.e., limited green cities > developing green cities > steady green cities > booming green cities, exhibiting the spatial characteristics of gradient differences between the coasts and inland areas, and the aggregation of blocks in some areas. (2) The supply and demand mismatches of the UPGS were relatively stable, with negative matching being the main supply mismatch type, and positive matching being the main demand mismatch type. The contribution of supply and demand mismatches similarly showed a spatial pattern of a gradual decrease from the coast to inland and the aggregation of blocks in some areas. (3) Five types of factors played different driving roles on UPGS, with social development remaining a weak factor, and the strong factor switching from urban infrastructure to construction land scale. The interaction detection was dominated by a bilinear enhancement, with super-interaction factors changing from an output value of the tertiary industry and population urbanization rate to education expenditure in local general public budgets. (4) Based on the mismatch between the supply and demand for UPGS in China, 286 cities were classified into four types, namely a smart shrinking zone, smart growing zone, status quo zone, and overlay policy zone, and differentiated development proposals for the corresponding zoning were put forward. This paper constructed an application framework of "evolution pattern + supply demand match + driving factors + policy zoning" for UPGS at a large scale, which will effectively enhance the effective allocation of its resources across the country. [ABSTRACT FROM AUTHOR]

Providing safe driving conditions has a great impact on reducing the amount of road accidents and deaths which are caused by them. The necessity of intelligent brake system for increasing the safety during driving has been taken into consideration in today's cars. The automatic emergency braking system is responsible for informing the driver of impending accidents and using the ultimate potential of the vehicle's braking before a collision occurs. In this paper, the purpose is to predict the brake based on brain EEG signals. For this purpose, the standard bnci database which is defined in this field is used. The aim of the proposed method of this article, is to predict emergency brake during simulated driving, using after error propagation neural network algorithm. The innovative aspect of this paper is the combined use of the dimension reduction algorithm, after-error propagation neural network, and training by the means of K cross validation algorithm for reducing neural network learning error. The proposed method is trained with dataset feature vectors so that after feature vector entry, test recognizes that if emergency brake is necessary or not. Results obtained from proposed method show that the accuracy of this method is more than 90 percent, which has a better performance in comparison with other methods. [ABSTRACT FROM AUTHOR]

Acute stress exposure can significantly impact a train driver's capacity to maintain safe train operations. However, research examining how train drivers perceive the impacts of acute stressors is limited. This study investigated train driver perceptions regarding performance impacts of stressful events and potential strategies for reducing negative impacts. 71 Australian train drivers were presented with three stressful event scenarios via an online survey and asked to rate the impacts on driving performance. Results showed that participants perceived that stress would enhance performance, but that impacts differed depending on the event type. The findings suggest that train drivers may not be subjectively aware of negative impacts of acute stress, which has important practical implications for risk management following an incident. Qualitative results revealed the most frequently reported stress impact related to cognition. Practical implications and future research directions to prevent and manage stressful event exposure are discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The trucking industry urgently requires comprehensive methods to evaluate driver safety, given the high incidence of serious traffic accidents involving trucks. The concept of a "truck driver persona" emerges as a crucial tool in enhancing driver safety and enabling precise management of road transportation safety. Currently, the road transport sector is only beginning to adopt the user persona approach, and thus the development of such personas for road transport remains an exploratory endeavor. This paper delves into three key aspects: identifying safety risk characteristic parameters, exploring methods for constructing personas and designing safety management interventions. Initially, bibliometric methods are employed to analyze safety risk factors across five domains: truck drivers, vehicles, roads, the environment, and management. This analysis provides the variables necessary to develop personas for road transportation drivers. Existing methods for constructing user personas are then reviewed, with a particular focus on their application in the context of road transportation. Integrating contemporary ideas in persona creation, we propose a framework for developing safety risk personas specific to road transportation drivers. These personas are intended to inform and guide safety management interventions. Moreover, the four stages of driver post-evaluation are integrated into the persona development process, outlining tailored safety management interventions for each stage: pre-post, pre-transit, in-transit, and on-post. These interventions are designed to be orderly and finely tuned. Lastly, we offer optimization recommendations and suggest future research directions based on safety risk factors, persona construction, and safety management interventions. Overall, this paper presents a safety management-oriented research technology system for constructing safety risk personas for truck drivers. We argue that improving the design of the persona index system, driven by big data, and encompassing the entire driver duty cycle-from pre-post to on-post-will significantly enhance truck driver safety. This represents a vital direction for future development in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Identifying factors that significantly affect drivers that are repeatedly involved in traffic violations or non-fatal crashes (defined here as recidivist drivers) is very important in highway safety studies. This study sought to understand the relationship between a set of variables related to previous driving violations and the duration between a previous non-fatal crash and a subsequent fatal crash, taking into account the age and gender of the driver. By identifying the characteristics of this unique driver population and the factors that influence the duration between their crash events strategies can be put in place to prevent the occurrence of future and potentially fatal crashes. To do this, a five-year (2015-2019) historical fatal crash data from the United States was used for this study. Out of 15,956 fatal crashes involving recidivist drivers obtained, preliminary analysis revealed an overrepresentation of males (about 75%). It was also found that the average duration between the two crash events was about a year and a half, with only an average of one month difference between male and female drivers. Using hazard-based duration models, factors such as number of previous crashes, previous traffic violations, primary contributing factors and some driver demographic characteristics were found to significantly be associated with the duration between the two crash events. The duration between the two events increased with driver's age for drivers who were involved in only one previous crash and the duration was shorter for those that were previously involved in multiple crashes. Previous DUI violations, license suspensions, and previous speeding violations were found to be associated with shorter durations, at varying degrees depending on the driver's age and gender. The duration was also observed to be longer if the fatal crash involved alcohol or drug use among younger drivers but shorter among middle-aged male drivers. These findings reveal interesting dynamics that may be linked to recidivist tendencies among some drivers involved in fatal crashes. The factors identified from this study could help identify crash countermeasures and programs that will help to reform such driver behaviors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers' decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants' final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A major safety hazard for e-bike riders crossing an intersection is encountering heavy vehicles turning right in the same direction, which often results in severe casualties. Recently, some cities in China have implemented right-turn safety improvement treatments (i.e., right-turn yielding rules and right-turn warning facilities) at intersections to reduce the occurrence of such accidents. However, the risk perception and behavior of e-bike riders and heavy vehicle drivers dynamically change during the right-turn interaction process, and the safety effects of different right-turn safety measures remain unclear. This study aims to investigate the safety effect of right-turn safety measures on E-Bike-Heavy Vehicle (EB-HV) right-turn conflicts at signalized intersections. The right-turn conflicts and potential influencing factors are extracted from aerial video data, including characteristics of right-turn warning facilities, characteristics and behavior of e-bike riders and heavy vehicle drivers, environmental factors, and traffic-related factors. Moreover, traffic conflict indicators such as the Time to Collision (TTC), Post Encroachment Time (PET), and Jerk are selected and calculated. Multinomial and binary logit models are used to estimate and analyze the EB-HV right-turn conflict severity and drivers yielding behavior. The results reveal that: (a) right-turn warning facilities can decrease the probability of slight and severe EB-HV right-turn conflicts, while the presence of law enforcement cameras could prompt heavy vehicle drivers to comply with the yielding rules and adopt more cautious behavior; (b) increased heavy vehicle speed and acceleration before turning right have strong correlation to illegitimate yielding behavior of the driver and higher EB-HV right-turn conflict severity; and (c) aggressive behavior of e-bike rider increases the severe conflict probability, especially at intersections without right-turn warning facilities. Based on the study findings, several practical implications are suggested to reduce the risk of EB-HV right-turn conflicts, enhance the effectiveness of right-turn safety measures, and improve crossing safety for e-bike riders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Driving behaviors are important cause of expressway crash. In this study, method based on modified time-to-collision (MTTC) to identify risky driving behaviors on an expressway diverge area is proposed, thus investigating the impact of velocity and acceleration features of risky driving behavior. Firstly, MTTC is applied to judge whether the behavior is risky. Then, the relationships between velocity, acceleration and different driving behavior on the expressway diverge area were fit by binary logistic regression models (BLR) with L2 regularization and random forests (RF) models, and the models were interpreted by feature importance plots and partial dependency plots. The results show that the AUC metric of 4 RF models for 4 types of driving behaviors, namely, left lane change, right lane change, acceleration and deceleration, are 0.932, 0.845, 0.846 and 0.860 separately. The interpretation of models demonstrates that velocity and absolute value of acceleration greatly affect the risk of the driving behaviors. Different driving behaviors with a certain acceleration have a range of safety speed range. The range will get narrower with the growth of maximum absolute value of acceleration rate, and will be nearly non-exist when the acceleration is over 5 m/s 2 . In conclusion, this study provided a methodology to measure the risk of driving behaviors and establish a model to recognize of risky driving behaviors. The results can lay the foundation for making countermeasures to prevent risky driving behaviors by managing the vehicle speed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Emotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers' emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers.

With the development of driving behavior monitoring technologies, commercial transportation enterprises have leveraged aberrant driving event detection results for evaluating crash risk and triggering proactive interventions. The state-of-the-art applications were established based upon instant associations between events and crash occurrence, which assumed crash risk surged with aberrant events. Consequently, the generated crash risk monitoring results merely contain discrete abrupt changes, failing to depict the time-varying trend of crash risk and posing challenges for interventions. Given the multiple types of aberrant events and their various temporal combinations, the key to depict crash risk time-varying trend is the analysis of multi-type events' temporal coupling influence. Existing studies employed event frequency to model combined influence, lacking the capability to differentiate the temporal sequential characteristics of events. Hence, there is an urgent need to further explore multi-type events' temporal coupling influence on crash risk. In this study, the temporal associations between multi-type aberrant driving events and crash occurrence are explored. Specifically, a contrastive learning method, fusing prior domain knowledge and empirical data, was proposed to analyze the single event temporal influence on crash risk. After that, a novel Crash Risk Evaluation Transformer (RiskFormer) was developed. In the RiskFormer, a unified encoding method for different events, as well as a self-attention mechanism, were established to learn multi-type events' temporal coupling influence. Empirical data from online ride-hailing services were employed, and the modeling results unveiled three distinct time-varying patterns of crash risk, including decay, increasing, and increasing-decay pattern. Additionally, RiskFormer exhibited remarkable crash risk evaluation performance, demonstrating a 12.8% improvement in the Area Under Curve (AUC) score compared to the conventional instant-association-based model. Furthermore, the practical utility of RiskFormer was illustrated through a crash risk monitoring sample case. Finally, applications of the proposed methods and their further investigations have been discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Stress impacts driving-related cognitive functions like attention and decision-making, and may arise in automated vehicles due to non-driving tasks. Unobtrusive relaxation techniques are needed to regulate stress without distracting from driving. Tactile wearables have shown efficacy in stress regulation through respiratory guidance, but individual variations may affect their efficacy. This study assessed slow-breathing tactile guidance under different stress levels on 85 participants. Physiological, behavioral and subjective data were collected. The influence of individual variations (e.g., driving habits and behavior, personality) using logistic regression analysis was explored. Participants could follow the guidance and adjust breathing while driving, but subjective efficacy depended on individual variations linked to different efficiency in using the technique, in relation with its attentional cost. An influence of factors linked to the evaluation of context criticality was also found. The results suggest that considering individual and contextual variations is crucial in designing and using such techniques in demanding driving contexts. In this line some design recommendations and insights for further studies are provided., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle-pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle-pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: 'driving risk index' and 'driving risk gradient,' facilitates the delineation of driving spaces for identifying safety-critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle-pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle-pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle-pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle-pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle-pedestrian interaction risks for safe and efficient vehicle navigation through an intersection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Urban arterial and collector roads, while interconnected within the urban transportation network, serve distinct purposes, leading to different driving risk profiles. Investigating these differences using advanced methods is of paramount significance. This study aims to achieve this by primarily collecting and processing relevant vehicle trajectory data alongside driver-vehicle-road-environment data. A comprehensive risk assessment matrix is constructed to assess driving risks, incorporating multiple conflict and traffic flow indicators with statistically temporal stability. The Entropy weight-TOPSIS method and the K-means algorithm are employed to determine the risk scores and levels of the target arterial and collector roads. Using risk levels as the outcome variables and multi-scale features as the explanatory variables, random parameters models with heterogeneity in means and variances are developed to identify the determinants of driving risks at different levels. Likelihood ratio tests and comparisons of out-of-sample and within-sample prediction are conducted. Results reveal significant statistical differences in the risk profiles between arterial and collector roads. The marginal effects of significant parameters are then calculated separately for arterial and collector roads, indicating that several factors have different impacts on the probability of risk levels for arterial and collector roads, such as the number of movable elements in road landscape pictures, the standard deviation of the vehicle's lateral acceleration, the average standard deviation of speed for all vehicles on the road segment, and the number of one-way lanes on the road segment. Some practical implications are provided based on the findings. Future research can be implemented by expanding the collected data to different regions and cities over longer periods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Traffic signs detection is an important and challenging task in intelligent driving perception system. This paper proposes an improved lightweight traffic signs detection framework based on YOLOv5. Firstly, the YOLOv5's backbone is replaced with ShuffleNet v2, which simplifies the calculation complexity and reduces the parameters of backbone network. Secondly, aiming at the problem of inconspicuous traffic sign characteristics in complex road environment, we use the CA attention mechanism in this paper to improve the saliency of the object. Finally, aiming at the large-scale difference between the traffic signs and the high proportion of small objects, we design the BCS-FPN to fuse multi-scale features and improve the representation ability of the small-scale objects. The TT-100K dataset is also analyzed and the dataset is collated. We test on the collated TT-100K dataset for the improved YOLOv5 in this paper. And the results show that compared with YOLOv5s, the mAP of our algorithm is equivalent to that of YOLOv5s, and the speed is improved by 20.8%. This paper also has carried on the experiment on embedded devices, experimental results show that our framework in computing power less embedded devices has a better effect., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: In 2016, large-scale 20 miles per hour speed limits were introduced in the United Kingdom cities of Edinburgh and Belfast. This paper investigates the role that scientific evidence played in the policy decisions to implement lower speed limits in the two cities., Methods: Using a qualitative case study design, we undertook content analysis of a range of documents to explore and describe the evolution of the two schemes and the ways in which evidence informed decision-making. In total, we identified 16 documents for Edinburgh, published between 2006 and 2016, and 19 documents for Belfast, published between 2002 and 2016., Findings: In both cities, evidence on speed, collisions and casualties was important for initiating discussions on large-scale 20 mph policies. However, the narrative shifted over time to the idea that 20 mph would contribute to a wider range of aspirations, none of which were firmly grounded in evidence, but may have helped to neutralize opposing discourses., Discussion and Conclusions: The relationship between evidence and decision-making in Edinburgh and Belfast was neither simple nor linear. Widening of the narrative appears to have helped to frame the idea in such a way that it had broad acceptability, without which there would have been no implementation, and probably a lot more push back from vested interests and communities than there was., (© 2024. The Author(s).)

Introduction: This study addresses the lack of methods to quantify driver familiarity with roadways, which poses a higher risk of crashes., Method: We present a new approach to assessing driving route diversity and familiarity using data from the DrivingApp, a smartphone-based research tool that collects trip-level information, including driving exposure and global positioning system (GPS) data, from young novice drivers (15-19 years old) to older drivers (67-78 years old). Using these data, we developed a GPS data-based algorithm to analyze the uniqueness of driving routes. The algorithm creates same route trip (SRT) arrays by comparing each trip of an identified user, employing statistically determined thresholds for GPS coordinate proximity and trip overlap. The optimal thresholds were established using a General Linear Model (GLM) to examine distance, and repeated observations. The Adjusted Breadth-First Search method is applied to the SRT arrays to prevent double counting or trip omission. The resulting list is classified as geographically distinct routes, or unique routes (URs)., Results: Manual comparison of algorithm output with geographical maps yielded an overall precision of 0.93 and accuracy of 0.91. The algorithm produces two main outputs: a measure of driving diversity (number of URs) and a measure of route-based familiarity derived from the Rescorla-Wagner model. To evaluate the utility of these measures, a Gaussian mixture model clustering algorithm was used on the young novice driver dataset, revealing two distinct groups: the low-frequency driving group with lower route familiarity when having higher route diversity, whereas the high-frequency driving group with the opposite pattern. In the older driver group, there was a significant correlation found between the number of URs and Geriatric Depression Score, or walking gait speed., Practical Applications: These findings suggest that route diversity and familiarity could complement existing measures to understand driving safety and how driving behavior is related to physical and psychological outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Introduction: An on-road study was conducted to examine the effects of level 2 automation on the stressfulness and enjoyment of driving and driving attention following prolonged usage. The study also examined the changes in the automated driving experience and attention over time as well as important predictors such as pre-driving trust in technology and attitudes toward automated systems., Method: Motorists who had never used automated systems drove a level 2 automation vehicle for a 6-8 week period., Results: Participants reported that the automated systems reduced the stress of driving and made traveling more enjoyable and relaxing. They also reported that the automation did not make traveling boring and take the fun out of driving. Participants indicated that their minds tended to wander when the automation was operating. The stressfulness of the automated driving experience decreased over time. Participants also reported feeling increasingly comfortable driving with the automation without monitoring it closely. The enjoyment and stress of automated driving is important because it shapes the willingness to use the automation and, hence, the safeness of driving. As expected, intentions to use and purchase automated systems were strongly predicted by the perceived favorableness of driving with the automation. Participants' pre-driving beliefs about automated systems, rather than their trust, appears to have shaped their experiences with the automation., Practical Applications: Although some of the findings suggest that automated systems increase unsafe behavior by novice users, other facets of the surveys suggest that motorists are cognizant of the risks of automated driving and discreet in their usage of the automation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)