Driver Profile Research Articles

How difficult is it perceived to takeover in a level 3 automated vehicle? Investigation of the ease of use according to task and situational factors

One goal of automated driving is to mitigate risks by minimising human intervention. However, widespread acceptance of automated vehicles hinges on their perceived ease of use, particularly during takeover scenarios. This online survey investigates the perceived difficulty of takeover in Level 3 Automated Vehicles, focusing on factors influencing takeover performance such as duration of automated mode and traffic density, as well as Non-Driving-Related Tasks like listening to music or engaging in conversation. Using Anderson’s experimental protocol based on Integrated Information Theory, 235 drivers aged 18–72 rated takeover difficulty across 32 realistic scenarios varying in these factors. Results indicate significant impacts of all factors on takeover perception, identifying three distinct driver profiles: takeover averse, adaptive, and confident. Findings underscore implications for vehicle acceptance and risk management in automated driving.

Simulation-based driver scoring and profiling system

This paper describes a rule-based Driver Scoring System model, derived from behavioral data collected using a driving simulator. It introduces a novel approach to establish driver profiles through feature engineering of acquired dataset, with features evaluating various aspects of driver behavior. The research aims to provide employers and drivers with profile-specific feedback and recommendations to design training protocols. Principal Component Analysis is applied on preprocessed dataset from 412 drivers for dimensionality reduction and feature selection. The K-means clustering algorithm is used for data analysis, resulting in three distinct clusters. The Kruskal-Wallis test, supplemented by post hoc Dunn testing is employed to determine statistical significance between clusters. Clusters are portrayed using descriptive statistics, specifically the mean scores and overall driver performance averages. Our method delineates three driver profiles, with two driving styles reflecting desirable driving skills and good overall performance, while the third represents unacceptable driving skills and bad overall performance.

The Driving Profile of Autistic Drivers and Their Driving Experiences: A Systematic Review.

A developmental milestone that both contributes to and facilitates a successful transition into adulthood is the ability to drive. Yet only one in three autistic adolescents successfully obtain a driver's license by age 21 compared to over three-fourths of non-autistic adolescents. Of those who receive their license, there is inconsistency in driving attitudes, experiences and skills. The current study aimed to (1) provide a systematic review of the driving profile of autistic drivers and (2) structurally organize driving performance within a theoretical driving model - Michon's Hierarchical Model of Driving. Following PRISMA guidelines, four databases were systematically searched to identify published articles related to autistic drivers. To be included, all studies met the following inclusion criteria: included autistic sample or people representing or working with autistic individuals, peer-reviewed, published between 2000 and 2021 and contained empirical data reporting on an aspect related to driving or motor vehicle transportation. A systematic search yielded 37 articles that met inclusion criteria. Based on the differential results derived from this review, it appears that autistic drivers are not generally at greater risk of negative driving outcomes; instead, they face unique challenges based on the skills necessary to navigate specific driving contexts. However, due to the inconsistency across results, the actual driving performance in autistic drivers remains unclear. Future research should focus on quantifying these results using an evidenced-based, theoretical framework to identify consistent strengths and challenges in driving performance across autistic drivers.

Two profiles of post-stroke drivers based on their driving perception

ObjectiveStroke is the leading cause of acquired disability in adults, and can induce different sequelae which impact on abilities such as driving. This study investigated how post-stroke patients perceived their driving practice and behavior. The aim of this study was to determine different profiles of post-stroke drivers according to their driving perception. MethodsAmong 86 post-stroke patients who had undergone an official fitness-to-drive assessment in 2019 in the Kerpape Center or the Henry Gabrielle Hospital (France), 69 returned to drive. These 69 participants completed a questionnaire about their socio-demographic and clinical characteristics, and driving perception, approximately three years after resuming driving. Questions about driving perception were statistically analyzed using hierarchical clustering analysis. Socio-demographic, driving, and clinical characteristics from the clusters obtained were then compared. ResultsClustering analysis revealed two profiles of post-stroke drivers. One group consisting of 2/3 of the participants reported modified driving behavior, while the other group perceived no driving changes. A higher proportion of participants from the modified driving group reported driving significantly less, experiencing less pleasure, increased tiredness and more fear when driving post-stroke than those from the unmodified driving group. They also stated that they experienced more difficulties, avoided more situations, and implemented more driving strategies than participants in the unmodified driving group. Participants in the modified driving group were also younger (p = 0.014), lived in more rural areas (p = 0.028), and had more sequelae, including fatigue (p = 0.002), motor sequelae (p = 0.035), and cognitive sequelae (p = 0.013) than participants who did not modify their driving. ConclusionsTwo profiles of post-stroke drivers were identified: those who had perceived the need to modify their driving behavior, and those who had perceived no driving changes. Differences in socio-demographic characteristics may partly explained the divergencies between the two groups. Other factors such as self-awareness of driving abilities may influence their driving perception. Two lines of action could be investigated to enable post-stroke drivers to better appreciate their driving abilities. The first would be to develop targeted support programs to help drivers become more aware of their own abilities, for example using video support during on-road training to replay critical situations and improve awareness of their own abilities. A second approach would be to deploy technological driving assistance systems. A monitoring system enabling the driver to adjust his driving in real time could be very useful and relevant to develop.

Naturalistic driving analysis of situational, behavioral, and psychosocial determinants of speeding

The present analysis used full-trip naturalistic driving data along with driver behavioral and psychosocial surveys to understand the individual and contextual predictors of speeding. The data were collected over a three-week period from 44 drivers and contain 3,798 full trips, with drivers speeding 7.8 % of the time. Speeding events were identified as periods when participants traveled at a velocity greater than five mph over the speed limit for at least five seconds. Data were analyzed using the Comprehensive Driver Profile (CDP) framework which uses principal component analysis (dimensionality reduction), random forest (predictive modeling), k-means clustering (grouping and profiling), and bootstrapping (profile stability) to decompose the predictive variables and driver characteristics. The final dataset included 188 candidate independent variables from the CDP framework and one dependent variable (speeding). Nine variables emerged as significant predictors of speeding onset with an AUC of 0.88, including the percent of trip time spent idling and speeding, highway driving in low traffic conditions, and positive attitudes toward phone use. Percent of trip speeding was associated with a higher likelihood of speeding by up to 42 percent, and percent trip idling was associated with it by up to 30 percent. Driver profile clusters revealed four types: Traffic & Idling Speeders, Infrequent Speeders, Frequent Speeders, and Situational Speeders. The present analysis demonstrates the importance of situational factors and individual differences in motivating speeding behavior. Countermeasures targeting speeding may be more effective if they address the root causes of the behavior in addition to the behavior itself.

Prevalence and Profiles of Risky Driving Behavior Among US Teenagers

This survey study updates prevalence data on risky driving behaviors in a nationally representative sample of young people in the US and characterizes profiles of drivers according to the types of risky behaviors they engaged in.

Effects of Tax Reform for Acceleration and Inclusion (TRAIN) Law Implementation Among Jeepney Drivers: Basis for Crafting a Financial Plan

Tax Reform for Acceleration and Inclusion (TRAIN) Law aspires to develop a tax system that is simpler, more just, more effective, and more efficient in order to fund government goals and initiatives. This study sought to answer the following: 1)The demographic profile of jeepney drivers in terms of: 1.1Age, 1.2Gender, 1.3Civil Status, and 1.4Number of family members. 2)The perceived effects of TRAIN Law among Jeepney Drivers in terms of: 2.1Income, and 2.2Expenses. 3)Significant difference on the perceived effects of Tax Reform for Acceleration and Inclusion (TRAIN) Law affecting the Jeepney Drivers when grouped according to their demographic profile. 4)The proposed financial plan for Jeepney Drivers. This quantitative study, a descriptive form, focused and assessed the effects of TRAIN law implementation on their financial aspects, notably their income and expenses. Purposive sampling was used to collect data from seventy-five (75) jeepney drivers who own and operate traditional jeepneys in San Jose Del Monte, Bulacan. The proposal of implementing a higher excise tax and a lower income tax to balance the tax flow is only favorable for just a few, but not for jeepney drivers. The researchers recommend the jeepney drivers to consider the financial plan provided, and the LGUs to conduct seminars on proper budgeting.

Sociodemographic and psychological factors affecting motor vehicle crashes (MVCs): a classification analysis based on the contextual-mediated model of traffic-accident involvement

The study aimed to determine the sociodemographic and psychological profiles of drivers with a history of motor vehicle crashes (MVCs), following the contextual-mediated model of crash involvement, and trying to define similarities and differences with drivers without MVCs. Although road trauma prevention has become a central public health issue, the study of psychological determinants of MVCs does not have consistent results due to methodological and theoretical weaknesses. Three-hundred and forty-five active drivers (20% females) completed an extensive office-based fitness-to-drive evaluation including measures of cognition, personality, self-reported driving-related behaviors, attitudes, as well as computerized measures of driving performance. The Classification and Regression Tree method (CART) was used to identify discriminant predictors. The classification identified several relevant predictors; the personality trait of Discostraint (as a distal context variable; cut-point: 50 T points) and motor speed (as a proximal context variable; cut-point: 64 percentile ranks). The global classification model increased approximately 3 times the probability of identifying people with a history of MVC involvement, starting from an estimated prevalence of being involved in an MVC in a period of five years in the population of active drivers. Consistent with the ‘contextual-mediated model of traffic accident involvement’, the results of the present study suggest that road trauma analysis should focus on both distal and proximal driver-related factors by paying attention to their association in determining MVCs. These results represent a valuable source of knowledge for researchers and practitioners for preventing road trauma.

Lightweight YOLOv8 Networks for Driver Profile Face Drowsiness Detection

Lightweight YOLOv8 Networks for Driver Profile Face Drowsiness Detection

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

Development of a nuanced approach to aggressive driving: An investigation into the self-regulatory and attitudinal profiles of aggressive drivers and their contextual role in negative road interactions

This study aimed to provide a novel perspective on aggressive driving behaviour (ADB) by developing a self-report measure of aggressive driving styles (the Aggressive Driving Styles Questionnaire; ADSQ) and comparing how different types of drivers (according to their attitudes and self-regulatory functioning) might differentially engage in such driving styles. An online survey was completed by a sample of Australian adult drivers (N = 527), who reported engaging in an ADB in the past month. An initial confirmatory factor analysis indicated that the proposed factor structure of instrumental, reactive, proactive and interpersonal driver aggression, was a good fit for the data, and reliability tests suggested that the ADSQ had good internal consistency (α = 0.73 - 0.91). Next, correlational analysis showed that the ADSQ had positive associations with (a) driver anger and rumination; (b) problematic attitudes towards instrumental and hostile aggressive driving; and (c) various domains (i.e., cognitive, attentional, emotional) of self-regulatory dysfunctioning. K-means cluster analysis was then used on the variables to determine potential profiles of ‘self-regulated’ drivers among the participants. The results suggested that three driver types significantly differed according to their attitudes and level of self-regulatory dysfunction: self-regulated drivers (high functioning; low attitudes), antisocial drivers (medium functioning; high attitudes), and dysregulated drivers (low functioning, high attitudes). These cluster groups were shown to significantly differ in their tendency to engage in aggressive driving styles and what behaviours they perceived as most triggering when driving on the road. The ADSQ and associated findings of this study provide a nuanced perspective on how ADBs can be approached, observed, and operationalised.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes

Objective Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. Method This study employed random parameters logit models, accommodating potential variations in parameter estimates’ means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. Results This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories – geometry and non-geometry factors. Conclusion These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Using machine learning to understand driving behavior patterns

Driver behavior is one of the principal factors associated with road accidents. Much research to date focusing on Machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors. In this paper, the development of an android mobile application (Driver Alert) is described, with the aim of collecting data from mobile phone sensors data to identify certain patterns and understand drivers’ behaviors. Additional information was recorded regarding weather and traffic information, using public API’s to complement the data directly collected from the vehicle. Four machine learning models (K-Means, Algorithm Agglomerative Hierarchical, Random Forest and Support Vector Machines) were tested and compared to identify different driver profiles. A native mobile application named DriverAlert was developed to support collect data and make it available, through an online dashboard, to drivers and researchers. Due to the available tools and libraries, it possesses, Python language was used, as it is a powerful programming language for workloads in data science, machine learning, and scientific computing.

Health perception and physical activity level profile of bus drivers of urban public transportation in Chapecó-SC.

Urban public transportation workers are of great social importance in cities. Working conditions can negatively affect the workers' health, causing illness and loss of quality of life. To analyze the perception of health and level of physical activity of bus drivers in urban public transportation in Chapecó, state of Santa Catarina. This study was characterized as descriptive, cross-sectional and field. It was carried out with 46 bus drivers, both genders, aged between 21 and 59 years. An anamnesis form was applied to characterize the participants and to assess the level of physical activity, the reduced International Physical Activity Questionnaire version VIII was used. The research took place in August and September 2021. Data analysis was performed descriptively, using means, standard deviation, relative and absolute frequency. There was a prevalence of 91.30% of practitioners of physical activity The perception of health considered good was 71.74%, being associated with the absence of chronic diseases. The predominant level of physical activity was physically active (63.05%). The vast majority of bus drivers presented a good perception of health, practiced physical activity, considered their work activities to be good and mentioned not taking medication for the treatment of chronic diseases.

Improving road safety at horizontal curves using V2I speed warning messages

Introduction: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions. Vehicle to Infrastructure (V2I) communications can address these limitations by providing drivers with valuable in-vehicle warning messages based on operational and safety data. There is limited effort investigating the impact of different types of V2I warning messages at horizontal curves and among different profiles of drivers. This study aims to thoroughly examine drivers’ behavior and compliance with different V2I warning messages when approaching horizontal curves. Methods: A driving simulator experiment and self-reported survey were conducted. Three main hypotheses were analyzed in this study. First, whether supplying drivers with in-vehicle V2I warning messages will positively affect drivers’ behavior at horizontal curves compared to the standard road signs. Second, whether there will be a significant difference in drivers’ behavior when receiving text and audio V2I warning messages. Third, whether seniors and female drivers will comply more with speed limit advisory provided through V2I message than younger and male drivers. Results: The Analysis of Covariance confirmed the first two hypotheses. Two main measures of drivers’ behavior found to be lower in the V2I communication scenarios compared to the base one. The audio warning message was found to be more promising in increasing drivers’ compliance with speed limit advisory when approaching the curves. Analyzing the third hypothesis revealed that younger and male drivers had higher curve initiation speed compared to females and seniors. Practical Applications: The findings of this study can be used by transportation researchers, authorities, and car manufacturers to improve the effectiveness of in-vehicle V2I warning messages among different profiles of drivers.

Assessment of musculoskeletal disorders and contributing factors in professional drivers

Musculoskeletal disorders (MSDs) are the leading global cause of disability and pose a significant public health issue, particularly among professional drivers who are prone to such conditions due to prolonged sitting, poor posture, and exposure to whole-body vibrations. The objectives of this study are to assess the demographic, general, lifestyle, and wellness profiles of professional drivers, gauge their work environment characteristics, and measure the proportion of musculoskeletal symptoms using the modified Nordic questionnaire. This descriptive study collected data from 67 professional drivers at major bus stands in Lahore using a structured questionnaire. The data were analyzed using frequencies, percentages, means, and standard deviations. The results revealed that the drivers, predominantly middle-aged (average age 39.72 ± 9.53 years) and married (95.52%), reported limited formal education (average 7.56 ± 3.91 years). Lifestyle assessments indicated high rates of smoking (61.19%) and frequent consumption of soft drinks (88.06%). In their work environment, most drivers (68.66%) worked daytime shifts, and a significant proportion (56.72%) experienced vibration exposure. The most frequently reported musculoskeletal symptoms were in the lower back (58.21% in the past 12 months, 43.28% in the past 7 days) and hips/thighs (61.19% in the past 12 months), impacting their ability to work. These findings highlight the critical need for targeted ergonomic and lifestyle interventions to improve the occupational health and safety of drivers, potentially reducing musculoskeletal discomfort and enhancing overall well-being and productivity.

Helical and non-helical large-scale dynamos in thin accretion discs

ABSTRACT The dynamics of accreting and outgoing flows around compact objects depends crucially on the strengths and configurations of the magnetic fields therein, especially of the large-scale fields that remain coherent beyond turbulence scales. Possible origins of these large-scale magnetic fields include flux advection and disc dynamo actions. However, most numerical simulations have to adopt an initially strong large-scale field rather than allow them to be self-consistently advected or amplified, due to limited computational resources. The situation can be partially cured by using sub-grid models where dynamo actions only reachable at high resolutions are mimicked by artificial terms in low-resolution simulations. In this work, I couple thin-disc models with local shearing-box simulation results to facilitate more realistic sub-grid dynamo implementations. For helical dynamos, detailed spatial profiles of dynamo drivers inferred from local simulations are used, and the non-linear quenching and saturation is constrained by magnetic helicity evolution. In the inner disc region, saturated fields have dipole configurations and the plasma β reaches ≃0.1 to 100, with correlation lengths ≃h in the vertical direction and ≃10 h in the radial direction, where h is the disc scale height. The dynamo cycle period is ≃40 orbital time scale, compatible with previous global simulations. Additionally, I explore two dynamo mechanisms which do not require a net kinetic helicity and have only been studied in shearing-box set-ups. I show that such dynamos are possible in thin accretion discs, but produce field configurations that are incompatible with previous results. I discuss implications for future general-relativistic magnetohydrodynamics simulations.

Machine learning approaches exploring the optimal number of driver profiles based on naturalistic driving data

Driver behavior analytics is an important concept that plays a significant role in the understanding of road crashes. This paper investigates the optimal number of driver profiles to understand the most important characteristics that differentiate drivers and extract useful insights on the value of using different clustering approaches in profile recognition. To this end, two Machine Learning clustering algorithms, the K-Means and OPTICS algorithms, are applied on driving data from a large naturalistic experiment using almost 18 K trips recorded from 130 drivers. The results revealed 3 profiles, the less risky drivers, the modest drivers and the more aggressive drivers. Clustering was based on 3 important driving behavior characteristics, namely the number of speeding, headway and harsh events per 100 km. The less risky drivers profile was revealed by both algorithms, whereas drivers of higher aggressiveness are distinguished by K-Means based on the driving feature that dominates the rest. The OPTICS algorithm showed that many drivers, especially the aggressive ones, present unique behavior that cannot be grouped together with other drivers. The interpretability of driver profiles resulting from the application of these unsupervised learning techniques is worsened as the number of clusters increases. The association between driver profiles and individual characteristics leads to the conclusion that aggressiveness is mainly driven by personality traits and less by specific characteristics such as gender, age or past accident history. The results of this study can be potentially used to develop profile-specific applications that provide feedback to drivers and reduce their crash risk.

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

Prevalence of work-related musculoskeletal disorders (WRMSDs) and its association with modifiable risk factors in metropolitan bus transit drivers: A cross-sectional comparison.

Bus drivers are one of the top three occupations with the highest prevalence of work-related musculoskeletal disorders (WRMSDs). The present study aimed to determine the pattern and prevalence of WRMSD among metropolitan bus transit drivers, whose job profiles differ from traditional long-distance bus drivers, and to explore the effect of modifiable lifestyle-related risk factors. In this cross-sectional study, consenting 254 metropolitan transit bus drivers (with at least five years of job duration) and 73 age-matched indoor desk job workers (administration staff of the same department) as a comparison group enrolled. Sociodemographic and occupational profile were collected on a semi-closed questionnaire. A modified Nordic questionnaire was used to determine musculoskeletal problems. Anthropometric measurement and haematocrit estimation were performed with standard techniques. All statistical analyses including logistic regression were performed with SPSS 26.0. The prevalence of WRMSDs among bus drivers was twice higher than administration staff (49.2% v/s 28.8%). Drivers experienced significantly higher pain for the lower back (36.6% v/s 11%), knee (29.5% v/s 15.1%), and hip (7.5% v/s 1.4%) in comparison with administration staff. Study reported age, tobacco usage, body mass index (BMI) and job profile of drivers (compared to administration staff) as significant predictors of WRMSDs. WRMSDs were significantly higher among metropolitan bus transit drivers in comparison with administrative staff. Furthermore, WRMSDs are strongly associated with tobacco use and BMI. These modifiable risk factors may be the targets for preventive strategies to reduce the burden of WRMSDs among bus drivers.