Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

Risk field modeling of urban tunnel based on APF.

OBJECTIVE: The purpose of this paper is to explore the changing laws of driving safety in the complex and changing driving environment in urban tunnels, to analyze the evolution of driving risk fields caused by changes in adjacent vehicles, driving behavior characteristics and road environment, and to reveal the formation mechanism of tunnel driving danger zones. METHODS: The kinetic field, behavioral field and potential field models are constructed according to the APF theory. The driving safety risks arising from the surrounding vehicles, driving behavior characteristics and changes in the tunnel environment are analyzed in the process of driving from the open section to the exit of the tunnel. RESULTS: The magnitude of the risk field force is inversely proportional to the spacing of the vehicles and the distance between the tunnel sidewalls, and is proportional to the relative speed between the vehicles and the slope of the longitudinal slope. Under the same conditions, the vehicle at the entrance and exit of the tunnel is subjected to a greater force of travel risk than inside the tunnel, and the effect of speed on the force of the risk field is greater than the distance. CONCLUSIONS: The established model better describes the trend of driving risk during the driving of vehicles in urban tunnels, and the research findings can provide theoretical support for the design and traffic management of urban tunnels.

Energy field-based lane changing behavior interaction model and risk evaluation in the weaving section of expressway.

OBJECTIVE: With the development of intelligent driving assistance systems, the evaluation of driving behavior risk has shifted from traditional single-vehicle studies to multi-vehicle studies. This study aimed to investigate the interaction mechanism between vehicles and to study the microscopic laws of traffic flow operation. METHODS: Firstly, the concept of "driving interaction field" was proposed. The virtual interaction quality and distance were used to define the driving interaction field. The interaction angle distinguished the vehicle interaction between different lanes. Then, the risk mechanism in the interaction process was analyzed by driving risk index. Corresponding thresholds of 50% and 85% quantile values were determined. Finally, the process of the lane-changing simulation experiments was divided into three phases (preparation, execution and adjustment). RESULTS: The driving risk index of the execution phase was larger than the other phases. Meanwhile, the comparison with the classical driving risk indexes revealed that the proposed index was more accurate and intuitive in describing the interaction risks. CONCLUSIONS: The driving interaction model proposed in this study quantified the overall environmental pressure on the vehicle. It overcomes the previous limitation of kinetic interaction parameters. The research provides a new idea for the ITS and autonomous driving systems, contributing to the enhancement of traffic safety and efficiency.

Rapid Return to Braking After Anterior and Posterior Approach Total Hip Arthroplasty.

BACKGROUND: Little is known about the effect of surgical approach on return to braking after total hip arthroplasty (THA), and few studies have investigated braking after THA with modern surgical techniques and rehabilitation protocols. METHODS: In a prospective comparative design, we enrolled 65 patients who received right-sided primary THA at our institution from April 2018 through March 2020, 34 with a direct anterior approach (DAA) and 31 with a posterior approach (PA). Braking tests measuring brake reaction time (BRT) and brake pedal depression (BPD) were administered to patients preoperatively and at 1, 2, and 4 weeks postoperatively using a realistic driving simulator. BRT and BPD were compared between groups and preoperatively versus postoperatively using mixed-effects models. RESULTS: Preoperative BRT averaged 638 msec in the DAA group and 604 msec in the PA group (P = 0.31). At 1 week postoperatively, the DAA group had significantly prolonged BRT compared with preoperatively (694 msec, P = 0.02). No significant difference was observed in the PA group (633 msec, P = 0.31). Both groups had returned to baseline by 2 weeks, and both had significantly faster BRT at 4 weeks compared with preoperatively (583 msec for DAA, P = 0.01; 537 msec for PA, P < 0.001). BPD was similar between groups, and there were no significant differences between preoperative and postoperative BPD at any time point. CONCLUSIONS: With modern surgical techniques, BRT after right-sided THA returns to baseline levels approximately 2 weeks after surgery. There seems to be a quicker return to preoperative BRT observed in patients with a PA.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception.

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

Outcome evaluation of the p drivers Program: Randomised controlled trial of a program to improve safe driving among novice drivers.

BACKGROUND: Globally, road traffic crashes are the leading cause of death for young adults. The P Drivers Project was a trial of a behavioural change program developed for, and targeted at, young Australian drivers in their initial months of solo driving when crash risk is at its highest. METHODS: In a parallel group randomised controlled trial, drivers (N = 35,109) were recruited within 100 days of obtaining their probationary licence (allowing them to drive unaccompanied) and randomised to an intervention or control group. The intervention was a 3 to 6-week multi-stage driving behaviour change program (P Drivers Program). Surveys were administered at three time points (pre-Program, approximately one month post-Program and at 12 months after). The outcome evaluation employed an on-treatment analysis comprising the 2,419 intervention and 2,810 control participants who completed all required activities, comparing self-reported crashes and police-reported casualty crashes (primary outcome), infringements, self-reported attitudes and behaviours (secondary outcomes) between groups. RESULTS: The P Drivers Program improved awareness of crash risk factors and intentions to drive more safely, relative to the controls; effects were maintained after 12-months. However, the Program did not reduce self-reported crashes or police-reported casualty crashes. In addition, self-reported violations, errors and risky driving behaviours increased in the intervention group compared to the control group as did recorded traffic infringements. This suggests that despite the Program increasing awareness of risky behaviour in novice drivers, behaviour did not improve. This reinforces the need to collect objective measures to accompany self-reported behaviour and intentions. CONCLUSIONS: The P Drivers Program was successful in improving attitudes toward driving safety but the negative impact on behaviour, lack of effect on crashes, and the large loss to follow-up fail to support the use of a post-licensing behaviour change program to improve novice driver behaviour and reduce crashes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: 363,293 (ANZCTR, 2012).

Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning.

To facilitate efficient transportation, I-4 Express is constructed separately from general use lanes in metropolitan area to improve mobility and reduce congestion. As this new infrastructure would undoubtedly change the traffic network, there is a need for more understanding of its potential safety impact. Unfortunately, many advanced real-time crash prediction models encounter an important challenge in their applicability due to their demand for a substantial volume of data for direct modeling. To tackle this challenge, we proposed a simple yet effective approach - anomaly detection learning, which formulates model as an anomaly detection problem, solves it through normality feature recognition, and predicts crashes by identifying deviations from the normal state. The proposed approach demonstrates significant improvement in the Area Under the Curve (AUC), sensitivity, and False Alarm Rate (FAR). When juxtaposed with the prevalent direct classification paradigm, our proposed anomaly detection learning (ADL) consistently outperforms in AUC (with an increase of up to 45%), sensitivity (experiencing up to a 45% increase), and FAR (reducing by up to 0.53). The most performance gain is attained through the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in an ensemble, resulting in a 0.78 AUC, 0.79 sensitivity, and a 0.22 false alarm rate. Furthermore, we analyzed model features with a game-theoretic approach illustrating the most correlated features for accurate prediction, revealing the attention of advanced convolution neural networks to occupancy features. This provided crucial insights into improving crash precaution, the findings from which not only benefit private stakeholders but also extend a promising opportunity for governmental intervention on the express lane. This work could promote express lane with more efficient resource allocation, real-time traffic management optimization, and high-risk area prioritization.

Investigating built environment and traffic flow impact on crash frequency in urban road networks.

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

A method to enhance drivers' hazard perception at night based on "knowledge-attitude-practice" theory.

During nighttime driving, the inherent challenges of low-illuminance conditions often lead to an increased crash rate and higher fatalities by impairing drivers' ability to recognize imminent hazards. While the severity of this issue is widely recognized, a significant research void exists with regard to strategies to enhance hazard perception under such circumstances. To address this lacuna, our study examined the potential of an intervention grounded in the knowledge-attitude-practice (KAP) framework to bolster nighttime hazard detection among drivers. We engaged a cohort of sixty drivers split randomly into an intervention group (undergoing specialized training) and a control group and employed a holistic assessment that combined eye movement analytics, physiological response monitoring, and driving performance evaluations during simulated scenarios pre- and post-intervention. The data showed that the KAP-centric intervention honed drivers' visual search techniques during nighttime driving, allowing them to confront potential threats with reduced physiological tension and ensuring more adept vehicle handling. These compelling findings support the integration of this methodology in driver training curricula and present an innovative strategy to enhance road safety during nighttime journeys.

State Department of Motor Vehicles Reporting Mandates of Dementia Diagnoses and Dementia Underdiagnosis.

Importance: With older drivers representing the fastest growing segment of the driver population and dementia prevalence increasing with age, policymakers face the challenge of balancing road safety and mobility of older adults. In states that require reporting a dementia diagnosis to the Department of Motor Vehicles (DMV), individuals with dementia may be reluctant to disclose symptoms of cognitive decline, and clinicians may be reluctant to probe for those symptoms, which may be associated with missed or delayed diagnoses. Objective: To assess whether DMV reporting policies for drivers with dementia are associated with primary care clinicians' underdiagnosing dementia. Design, Setting, and Participants: This cross-sectional study used data from the 100% Medicare fee-for-service program and the Medicare Advantage plans from 2017 to 2019 on 223 036 primary care clinicians with at least 25 Medicare patients. Statistical analysis was performed from July to October 2023. Exposures: State DMV reporting policies for drivers with dementia. Main Outcomes and Measures: The main outcome was a binary variable indicating whether the clinician underdiagnosed dementia or not. Each clinician's expected number of dementia cases was estimated using a predictive model based on patient characteristics. Comparing the estimation with observed dementia diagnoses identified clinicians who underdiagnosed dementia vs those who did not, after accounting for sampling errors. Results: Four states have clinician reporting mandates, 14 have mandates requiring drivers to self-report dementia diagnoses, and 32 states and the District of Columbia do not have explicit requirements. Among primary care clinicians in states with clinician reporting mandates (n = 35 620), 51.4% were female, 91.9% worked in a metropolitan area, and 19.9% of the patient panel were beneficiaries dually eligible for Medicare and Medicaid. Among primary care clinicians in states with patient self-reporting mandates (n = 57 548), 55.7% were female, 83.1% worked in a metropolitan area, and 15.4% of the patient panel were dually eligible for Medicare and Medicaid. Among clinicians in states without mandates, 55.7% were female, 83.0% worked in a metropolitan area, and 14.6% of the patient panel were dually eligible for Medicare and Medicaid. Clinicians in states with clinician reporting mandates had an adjusted 12.4% (95% CI, 10.5%-14.2%) probability of underdiagnosing dementia compared with 7.8% (95% CI, 6.9%-8.7%) in states with self-reporting and 7.7% (95% CI, 6.9%-8.4%) in states with no mandates, an approximately 4-percentage point difference (P < .001). Conclusions and Relevance: Results of this cross-sectional study of primary care clinicians suggest that mandatory DMV policies for clinicians to report patients with dementia may be associated with a higher risk of missed or delayed dementia diagnoses. Future research is needed to better understand the unintended consequences and the risk-benefit tradeoffs of these policies.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Effects of executive load on crashes and near-crashes for young versus older drivers.

With the increasing use of infotainment systems in vehicles, secondary tasks requiring executive demand may increase crash risk, especially for young drivers. Naturalistic driving data were examined to determine if secondary tasks with increasing executive demand would result in increasing crash risk. Data were extracted from the Second Strategic Highway Research Program Naturalistic Driving Study, where vehicles were instrumented to record driving behavior and crash/near-crash data. executive and visual-manual tasks paired with a second executive task (also referred to as dual executive tasks) were compared to the executive and visual-manual tasks performed alone. Crash/near-crash odds ratios were computed by comparing each task condition to driving without the presence of any secondary task. Dual executive tasks resulted in greater odds ratios than those for single executive tasks. The dual visual-manual task odds ratios did not increase from single task odds ratios. These effects were only found in young drivers. The study shows that dual executive secondary task load increases crash/near-crash risk in dual task situations for young drivers. Future research should be conducted to minimize task load associated with vehicle infotainment systems that use such technologies as voice commands.

A multimodal physiological dataset for driving behaviour analysis.

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.

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

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

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Knowing me, knowing you-A study on top-down requirements for compensatory scanning in drivers with homonymous visual field loss.

OBJECTIVE: It is currently still unknown why some drivers with visual field loss can compensate well for their visual impairment while others adopt ineffective strategies. This paper contributes to the methodological investigation of the associated top-down mechanisms and aims at validating a theoretical model on the requirements for successful compensation among drivers with homonymous visual field loss. METHODS: A driving simulator study was conducted with eight participants with homonymous visual field loss and eight participants with normal vision. Participants drove through an urban surrounding and experienced a baseline scenario and scenarios with visual precursors indicating increased likelihoods of crossing hazards. Novel measures for the assessment of the mental model of their visual abilities, the mental model of the driving scene and the perceived attention demand were developed and used to investigate the top-down mechanisms behind attention allocation and hazard avoidance. RESULTS: Participants with an overestimation of their visual field size tended to prioritize their seeing side over their blind side both in subjective and objective measures. The mental model of the driving scene showed close relations to the subjective and actual attention allocation. While participants with homonymous visual field loss were less anticipatory in their usage of the visual precursors and showed poorer performances compared to participants with normal vision, the results indicate a stronger reliance on top-down mechanism for drivers with visual impairments. A subjective focus on the seeing side or on near peripheries more frequently led to bad performances in terms of collisions with crossing cyclists. CONCLUSION: The study yielded promising indicators for the potential of novel measures to elucidate top-down mechanisms in drivers with homonymous visual field loss. Furthermore, the results largely support the model of requirements for successful compensatory scanning. The findings highlight the importance of individualized interventions and driver assistance systems tailored to address these mechanisms.

A systematic review of the use of in-vehicle telematics in monitoring driving behaviours.

BACKGROUND: Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed. METHODOLOGY: Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of "good" in the quality assessment. RESULTS: We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review. CONCLUSION: We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.

Behind the wheel: Probing into personality, skills, and driving behavior's role in bus rapid transit crashes.

OBJECTIVE: Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers. METHODS: The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method. RESULT: The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving. CONCLUSION: The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.