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.

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.

Interrelatedness of steering and lateral position parameters: Recommendations for the assessment of driving performance.

INTRODUCTION: Loss of attention leads to less steady driving within the lane and is one of the main causes of road accidents. To improve road safety, vehicle-based parameters such as steering wheel angle and lateral position are used to objectively assess driving performance, especially in monotonous driving tasks. METHOD: The present driving simulator study investigated the extent to which eight commonly used parameters are independent indicators of driving performance. Fifteen participants undertook a monotonous highway driving task for 1 h. Four steering angle parameters were examined: average steering angle (ASA), standard deviation of steering angle (SDSA), steering angle range (SAR), and steering reversal rate (SRR); as well as four lateral position parameters: mean lateral position (MLP), standard deviation of lateral position (SDLP), lateral position range (LPR), and the out-of-lane duration. Measurements were averaged across 2-minute epochs. Repeated measures correlation analysis evaluated the similarity between each parameter, and the variance inflation factor test evaluated the multicollinearity of all the parameters. RESULTS: The results demonstrated that some parameters are highly correlated and should not be used together to assess driving performance. It is recommended that the optimal combination is ASA and SAR to assess steering angle, and SDLP and out-of-lane to assess lateral position. Out-of-lane, as a factor directly contributing to road safety, is recommended because it has the least correlation with other parameters. PRACTICAL APPLICATIONS: If implemented, these recommendations may improve the assessment of driving performance in future studies.

How job stressors and economic stressors impact public transport drivers' performance and well-being under the health risk of the COVID-19 pandemic.

INTRODUCTION: During the COVID-19 pandemic, public transport (e.g., bus and taxi) drivers encountered great stress because they needed to work to maintain the operation of the transportation system. This study proposes and empirically investigates the impacts of job stressors and economic stressors of public transport drivers on emotional exhaustion, and subsequent psychological well-being and performance under the health risk of COVID-19. The moderating effects of perceived threat and death anxiety on the relationships between stressors and emotional exhaustion are also examined. METHOD AND RESULTS: Using two survey samples collected from bus and taxi drivers in Taiwan, the results reveal that, except for the effect of time pressure on taxi drivers' exhaustion, job stressors (job overload and time pressure) and economic stressors (job insecurity) positively relate to emotional exhaustion for both bus and taxi drivers. Drivers' emotional exhaustion has negative effects on both job satisfaction and positive effects on risky driving behaviors. Perceived pandemic threat strengthens the positive influence of job insecurity on emotional exhaustion for bus drivers, while perceived pandemic threat and death anxiety weaken the negative influence of job insecurity on emotional exhaustion for taxi drivers. PRACTICAL APPLICATIONS: Effective intervention strategies and policies to mitigate perceived pandemic threat and death anxiety of drivers are recommended.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios.

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

Could driving help us to "see better"? A comparative assessment of saccadic efficiency, visual speed, and attention.

BACKGROUND: This study aimed at comparing drivers' and non-drivers' results in the Adult Developmental Eye Movement with Distractors test (ADEMd) and the Useful Field of View test (UFOV). METHODS: One hundred and twenty Spaniards (mean age 50.90 ± 17.32 years) without eye disease voluntarily participated in this cross-sectional descriptive study. Participants in a single experimental session completed a questionnaire on sociodemographic, health, eyesight, and driving information. They also performed the ADEMd and UFOV tests randomly following standardized protocols. The ADEMd is a visual-verbal test that measures saccadic efficiency and visual attention. Brown-Forsythe (B-F) tests with Games-Howell post-hoc adjustments were conducted to assess differences between groups. Groups were formed according to sex, age (young adults, adults, and older adults), and driver/non-driver for further analysis. Additionally, associations between dependent variables were assessed through Spearman's correlations. RESULTS: Drivers obtained significantly better results in the ADEMd compared with non-drivers. Non-significant differences between drivers and non-drivers were encountered in the UFOV. Additionally, significant differences were observed between sexes and age groups. It is worth highlighting that non-driver's age significantly correlated with worse ADEMd performance (rho = .637 to .716). This correlation was non-significant in drivers. Similarly, reading hours significantly correlated with better ADEMd performance in non-drivers (rho = - .291 to - .363), but not in drivers. The only significant correlations between ADEMd and UFOV tests were found in drivers (rho = .307 to .410). CONCLUSION: Considering all the discussed results, it could be hypothesized that the driving task promotes abilities, such as oculomotor and cognitive function, which are relevant for the performance in the ADEMd. However, this hypothesis is based on correlational outcomes and further studies should causally assess this possible relation.

Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment.

Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. Our study presents a Distracted Driving Classification (DDC) approach utilizing a visual Large Language Model (LLM), named the Distracted Driving Language Model (DDLM). The DDLM introduces whole-body human pose estimation to isolate and analyze key postural features-head, right hand, and left hand-for precise behavior classification and better interpretability. Recognizing the inherent limitations of LLMs, particularly their lack of logical reasoning abilities, we have integrated a reasoning chain framework within the DDLM, allowing it to generate clear, reasoned explanations for its assessments. Tailored specifically with relevant data, the DDLM demonstrates enhanced performance, providing detailed, context-aware evaluations of driver behaviors and corresponding risk levels. Notably outperforming standard models in both zero-shot and few-shot learning scenarios, as evidenced by tests on the 100-Driver dataset, the DDLM stands out as an advanced tool that promises significant contributions to driving safety by accurately detecting and analyzing driving distractions.

Safety assessment of on-road cycling lanes: A comparative study of different layouts using driving simulator.

Over the past few decades, a growing attention has been directed toward cycling due to its positive impacts on social, economic, and health aspects. Various countries are adopting and implementing strategies to promote cycling as a daily mode of transport. The main objective of this study is to improve cyclists' safety by investigating the impact of different layouts of on-road cycle lanes at two-lane two-way roads on drivers' interactions with cyclists using driving simulator. Three layouts of on-road cycle lanes were tested and compared, namely, uncolored, colored, and island separation, along with a control case where no cycle lane was provided. In addition, the impact of road alignments (straight sections, left and right curves) and the presence of an opposing vehicle were investigated. The driving simulator at Qatar University was used to conduct this study. A total of 92 subjects participated in this study. According to the results, on-road cycle lanes can significantly increase the safety of cyclists compared to shared lanes with motorized traffic. Moreover, the results showed that the drivers' intrusion to the opposite lane in the presence of opposing vehicles can be eliminated by providing on-road cycle lanes. That is, drivers' crash risk can also be reduced through the provision of on-road cycle lanes. Comparison of different on-road cycle lane treatments showed that uncolored cycle lanes outperformed the other layouts in terms of lateral clearance between the driver and the cyclist for right and straight alignments. On the other hand, the colored cycle lane showed better results for the left alignment. The findings of this study could be useful for designing on-road bicycle infrastructure to eliminate possible vehicle-cyclist and vehicle-vehicle conflicts and minimize crash risk.

A multi-fidelity approach for reliability-based risk assessment of single-vehicle crashes.

Road vehicles are highly susceptible to single-vehicle crashes (SVCs) under complex road geometry and inclement weather, which can significantly threaten traffic safety and mobility of the whole traffic system. Most existing studies involve various simplifications and approximations to assess the associated SVC risks promptly, and therefore the assessment accuracy is often compromised. A novel multi-fidelity approach is developed for the reliability-based risk assessment of SVCs to balance the simulation accuracy and efficiency. Specifically, a high-fidelity transient dynamic vehicle model is introduced for a robust estimation of the vehicle dynamics under various driving environments, assisted by a low-fidelity simplified physics-based vehicle model to improve the computational efficiency. Based on the simulations of the two models, a new multi-fidelity improved cross entropy-based importance sampling (MFICE) algorithm is proposed for integrating multi-fidelity information and facilitating accurate and efficient reliability analysis. Five demonstrative cases are studied to evaluate the performance of the proposed approach, including the comparison with existing representative approaches. The results show that the proposed innovative multi-fidelity approach can provide a reliability evaluation of SVCs both accurately and efficiently, with obviously superior performance over typical state-of-the-art counterparts. Therefore, the proposed approach bears great potential on developing proactive and near real-time intelligent traffic operation and management strategies against SVCs in both normal and hazardous conditions.

An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving.

By the year 2045, it is projected that Autonomous Vehicles (AVs) will make up half of the new vehicle market. Successful adoption of AVs can reduce drivers' stress and fatigue, curb traffic congestion, and improve safety, mobility, and economic efficiency. Due to the limited intelligence in relevant technologies, human-in-the-loop modalities are still necessary to ensure the safety of AVs at current or near future stages, because the vehicles may not be able to handle all emergencies. Therefore, it is important to know the takeover readiness of the drivers to ensure the takeover quality and avoid any potential accidents. To achieve this, a comprehensive understanding of the drivers' physiological states is crucial. However, there is a lack of systematic analysis of the correlation between different human physiological responses and takeover behaviors which could serve as important references for future studies to determine the types of data to use. This paper provides a comprehensive analysis of the effects of takeover behaviors on the common physiological indicators. A program for conditional automation was developed based on a game engine and applied to a driving simulator. The experiment incorporated three types of secondary tasks, three takeover events, and two traffic densities. Brain signals, Skin Conductance Level (SCL), and Heart Rate (HR) of the participants were collected while they were performing the driving simulations. The Frontal Asymmetry Index (FAI) (as an indicator of engagement) and Mental Workload (MWL) were calculated from the brain signals to indicate the mental states of the participants. The results revealed that the FAI of the drivers would slightly decrease after the takeover alerts were issued when they were doing secondary tasks prior to the takeover activities, and the higher difficulty of the secondary tasks could lead to lower overall FAI during the takeover periods. In contrast, The MWL and SCL increased during the takeover periods. The HR also increased rapidly at the beginning of the takeover period but dropped back to a normal level quickly. It was found that a fake takeover alert would lead to lower overall HR, slower increase, and lower peak of SCL during the takeover periods. Moreover, the higher traffic density scenarios were associated with higher MWL, and a more difficult secondary task would lead to higher MWL and HR during the takeover activities. A preliminary discussion of the correlation between the physiological data, takeover scenario, and vehicle data (that relevant to takeover readiness) was then conducted, revealing that although takeover event, SCL, and HR had slightly higher correlations with the maximum acceleration and reaction time, none of them dominated the takeover readiness. In addition, the analysis of the data across different participants was conducted, which emphasized the importance of considering standardization or normalization of the data when they were further used as input features for estimating takeover readiness. Overall, the results presented in this paper offer profound insights into the patterns of physiological data changes during takeover periods. These findings can be used as benchmarks for utilizing these variables as indicators of takeover preparedness and performance in future research endeavors.

Proposing an effective approach for traffic safety assessment on heterogeneous traffic conditions using surrogate safety measures and speed of the involved vehicles.

OBJECTIVE: The vehicular traffic in the cities is becoming more complex in developing countries like India due to the rising population and rapid urbanization. With the annually increasing road accidents, a study on the effective safety assessment of heterogeneous traffic conditions is needed. The study aims to evaluate the safety of heterogeneous traffic by spotting the critical conflicts with respect to the speed of the involved vehicles. METHODS: The current study proposed Critical Following Speed instead of using a single threshold value for safety assessment in mixed traffic. Critical Following Speed was proposed by comparing the stopping and the available distances between the involved vehicles and used to identify the critical conflicts. With this, the study uses the speeds of both the leading and the following vehicles to judge the nature of the conflict. Three unsignalized four-legged intersections (S-1, S-2, and S-3) and two straight road sections (S-4 and S-5) were selected as the study area in Trichy, India. Post Encroachment Time (PET) and Time to Collison (TTC) were used as surrogate indicators to assess the crossing or merging and rear-end conflicts, respectively. RESULTS: The average PET and TTC values were between 1.25 and 1.73 s in the study locations. The overall percentage of critical conflicts indicated the non-safer crossing and merging maneuvers in three locations. The other two locations were experiencing safe rear-end conflicts, as the percentages of critical conflicts were below 4%. Various combinations of leading and following vehicle types were examined for the contribution of critical conflicts. The proposed methodology was validated with the 4-year accident data and a good relation was obtained. CONCLUSIONS: Fast-moving vehicles were responsible for the less safe maneuvers with a higher collision probability in all the study locations. The correlation between critical conflicts and road accidents shows the effectiveness of the proposed approach in the traffic safety assessments for mixed traffic. This approach could be employed even in countries with homogenous traffic conditions instead of using a single threshold value. The correlations also show the potential of the proposed Critical Following Speed as a surrogate safety indicator for safety evaluation in the future.

Critical voxel learning with vision transformer and derivation of logical AV safety assessment scenarios.

Safety assessment is an active research subject for autonomous vehicles (AVs) that have emerged as a new mode of mobility. In particular, scenario-based safety assessments have garnered significant attention. AVs can be tested on how they safely avoid hypothetical situations leading to accidents. However, scenarios written by humans based on their expert knowledge and experience may only partially reflect real-world situations. Instead, we are keen on a different technique of extracting statistically significant and more detailed scenarios from sensor data captured during the critical moments when AVs become vulnerable to potential accidents. Specifically, we first render the three-dimensional space around an AV with fixed-sized voxels. Then, we modeled the aggregate kinetics of the objects in each voxel detected by 3D-LiDAR sensors mounted on real test AVs. The Vision Transformer we used to model the kinetics helped us quickly pinpoint critical voxels containing objects that threatened the AV's safety. We traced the trajectory of the critical voxels on a visual attention map to describe in detail how AVs become vulnerable to accidents according to the logical scenario format defined by the PEGASUS Project. We tested our novel method with 250 h of 3D-LiDAR recordings capturing critical moments. We devised an inference model that detected critical situations with an F1-score of 98.26%. For each type of scenario, our model consistently identified the critical objects and their tendency to influence AVs. Given the evaluation results, we can ensure that our data-driven approach yields an AV safety assessment scenario with high representativeness, coverage, expansion, and computational feasibility.

An assessment of court fees, surcharges, and penalties for alcohol-impaired driving in five midwestern U.S. states: implications for exacerbating poverty and health inequalities.

Driving under the influence (DUI) remains an important threat to public health in the United States, and a substantial literature has evaluated the effectiveness of state-mandated penalties. Researchers have overlooked accelerated use of obscured fees and surcharges levied by local and state court systems added to penalties in the past 15 years. We present data regarding DUI penalties for offenders with a blood alcohol content (BAC of 0.08) and the fees and surcharges attached to them in Minnesota, Illinois, Wisconsin, Michigan, and Iowa, and variation in these within Wisconsin at four BAC levels. In all states, surcharges and fees exceed penalty fines substantially. Variation within Wisconsin is also meaningful. Our data suggest that opaque costs in state court systems add a substantial financial burden to DUI penalties, particularly for those with lower incomes. An appraisal of the deterrent role of these added costs is warranted.

Bibliometric review of telematics-based automobile insurance: Mapping the landscape of research and knowledge.

Telematics technology and its implementation in auto insurance have received great interest due to their potential to transform the insurance sector and promote safer driving practices. By implementing telematics technology, insurers may tailor insurance premiums to individual drivers, taking into account their real driving habits and performance, ultimately leading to improved road safety, cost savings, and an empowered driving community. The current study, through bibliometric analysis, carefully identifies and evaluates the existing body of scholarly literature on this subject for the last 21 years, including journal articles, conference papers, and related publications. The analysis uncovers key research studies, influential authors, top publication outlets, top countries with collaborations, and prolific research fields, providing useful insights into the evolution and growth of telematics-based insurance research. Furthermore, thematic mapping, cluster analysis, and critical analysis of top recent studies aided in identifying key research clusters and themes, as well as potential gaps and areas for further exploration, guiding future researchers and policymakers in advancing this transformative technology.

Planning traffic surveillance in Spain: How to optimize the management of police resources to reduce road fatalities.

Although traffic police enforcement is widely recognized as a key action in the road safety field, it can be a costly policy to implement. In addition, governments often impose budget constraints that can limit the resources available for activities such as law enforcement and surveillance. To evaluate the impact of human traffic control resources planning on traffic fatalities on Spanish NUTS-3 regions interurban roads, this paper uses an econometric model to investigate the performance of police enforcement intensity by focusing on two crucial traffic law infractions (i.e., speeding and drunk driving). After controlling for a range of economic, demographic, climate, and risk exposure variables, results highlight the relevance of visible, human, and in-person traffic law enforcement through regular vehicle patrols for reducing traffic crashes, with a non-significant effect of automatic enforcement. Our findings have important implications for traffic police resource management regarding the effective maintenance of patrol cars and plans to digitalize and automatize police administrative tasks and procedures.

Social and Neighborhood Context Moderates the Associations Between Processing Speed and Driving Mobility: A 10-year Analysis of the ACTIVE Study.

Objectives: Processing speed is essential to functional independence in later life, such as driving a vehicle. Few studies have examined processing speed and driving mobility in the context of racial differences and social determinants of health (SDoH). This study characterized the longitudinal association between processing speed and driving mobility, and how it varied by race and SDoH. Methods: Using data from the control arm of the Advanced Cognitive Training in Vital Elderly study (n = 581, 24.5% Black), multilevel models examined longitudinal associations between processing speed and driving mobility outcomes (driving space, exposure, and difficulty). Race and SDoH moderations were explored. Results: Decline in processing speed measures was associated with increased self-reported driving difficulty, but only for older adults with below-average to average scores for neighborhood and built environments and social community context SDoH domains. Discussion: Findings emphasize the influence of physical and social environmental characteristics on processing speed and driving mobility.

A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.

When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.

FEEDBACK trial - A randomised control trial to investigate the effect of personalised feedback and financial incentives on reducing the incidence of road crashes.

BACKGROUND: Road crashes continue to pose a significant threat to global health. Young drivers aged between 18 and 25 are over-represented in road injury and fatality statistics, especially the first six months after obtaining their license. This study is the first multi-centre two-arm parallel-group individually randomised controlled trial (the FEEDBACK Trial) that will examine whether the delivery of personalised driver feedback plus financial incentives is superior to no feedback and no financial incentives in reducing motor vehicle crashes among young drivers (18 to 20 years) during the first year of provisional licensing. METHODS: A total of 3,610 young drivers on their provisional licence (P1, the first-year provisional licensing) will participate in the trial over 28 weeks, including a 4-week baseline, 20-week intervention and 4-week post-intervention period. The primary outcome of the study will be police-reported crashes over the 20-week intervention period and the 4-week post-intervention period. Secondary outcomes include driving behaviours such as speeding and harsh braking that contribute to road crashes, which will be attained weekly from mobile telematics delivered to a smartphone app. DISCUSSION: Assuming a positive finding associated with personalised driver feedback and financial incentives in reducing road crashes among young drivers, the study will provide important evidence to support policymakers in introducing the intervention(s) as a key strategy to mitigate the risks associated with the burden of road injury among this vulnerable population. TRIAL REGISTRATION: Registered under the Australian New Zealand Clinical Trials Registry (ANZCTR) - ACTRN12623000387628p on April 17, 2023.

Addressing the Complex Driving Needs of an Aging Population.

This JAMA Insights reviews the complex driving needs of older individuals and how clinicians can help address these needs, including recommending use of supplemental technology, assessing fitness to drive, and reviewing medications that may impair driving ability.