Skilled Driver Research Articles

Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability.

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article provides a systematic review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that HRV and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

Identifying vehicle characteristic target based on subjective ratings from drivers

In virtual tire development, defining vehicle characteristic target is crucial to achieving the desired vehicle performance with the equipped tires. Vehicle performance is generally evaluated through subjective ratings by skilled drivers following SAE J1441 guidelines, as well as the objective performance metrics obtained via vehicle dynamics analysis. However, due to the scarcity and bias in real-world testing dataset, the vehicle characteristic target cannot be determined definitively. To address this issue, we propose a recommendation system that searches the unexplored vehicle characteristic domains. This suggested method provides virtual subjective ratings to enrich the limited real-world dataset from the skilled drivers. Moreover, we highlight the correlation between subjective and objective data using vehicle and tire dynamics simulation software. Based on this augmented data, we predict the vehicle characteristic target according to the subjective rating requirements. The proposed approach was validated through various experiments involving skilled drivers and various vehicle-tire combinations, showing 91.26% to 96.2% consistency in generated steering parameter domains. Domains of stability parameters show that one fewer domain was generated by a conservative driver compared to more lenient driver, which confirms the robustness of the proposed approach in accommodating individual driver preferences.

Assessing the relationship between driving skill, driving stress, and driving behavior

Introduction: Driving skill, driving stress, and driving behavior have an effect on road traffic safety. Most previous studies have investigated the effect of driving skill or driving stress on driving behavior separately. However, the relationship and effect mechanism between driving skill, driving stress, and driving behavior have been neglected. Method: 1207 licensed drivers completed a valid questionnaire, which consisted of driving skill subscale, driving stress subscale, and driving behavior subscale, and eight factors were identified from the three subscales. This study used correlation analysis, regression analysis to explore the relationship between driving skill, driving stress, and driving behavior, and examined the mediating and moderating roles of driving stress. Results: (a) There was a significant relationship between driving skill, driving stress, and driving behavior. (b) Highly skilled drivers showed decreased driving tension, thrill seeking, dislike of driving and increased hazard monitoring, and reported fewer lapses and errors, but more violations. Driving tension, thrill seeking increased aberrant driving behavior, while hazard monitoring helped reduce aberrant behaviors. (c) Driving stress had a mediating effect between driving skill on lapses and errors. Hazard monitoring and dislike of driving had a suppressing effect between driving skill on violations. (d) The positive effect of driving skill on violations was enhanced under positive driving tension and thrill seeking. Conclusions: Aberrant driving behavior was influenced by driving skill and driving stress. Driving skill influenced driving behavior through driving tension, hazard monitoring, and dislike of driving. Driving tension and thrill seeking moderated the relationship between driving skill on violations. Practical Applications: This study further explored the relationship between driving skill, driving stress, and driving behavior. It provides guidance for reducing aberrant driving behavior on driver education, that is, paying more attention to improving driving skill, as well as developing safety attitudes and self-regulation ability.

Serial visual reversal learning in captive black-handed spider monkeys, Ateles geoffroyi

Recent research suggests that socio-ecological factors such as dietary specialization and social complexity may be drivers of advanced cognitive skills among primates. Therefore, we assessed the ability of 12 black-handed spider monkeys (Ateles geoffroyi), a highly frugivorous platyrrhine primate with strong fission-fusion dynamics, to succeed in a serial visual reversal learning task. Using a two-alternative choice paradigm we first trained the animals to reliably choose a rewarded visual stimulus over a non-rewarded one. Upon reaching a pre-set learning criterion we then switched the reward values of the two stimuli and assessed if and how quickly the animals learned to reverse their choices, again to a pre-set learning criterion. This stimulus reversal procedure was then continued for a total of 80 sessions of 10 trials each. We found that the spider monkeys quickly learned to reliably discriminate between two simultaneously presented visual stimuli, that they succeeded in a visual reversal learning task, and that they displayed an increase in learning speed across consecutive reversals, suggesting that they are capable of serial reversal learning-set formation with visual cues. The fastest-learning individual completed five reversals within the 80 sessions. The spider monkeys outperformed most other primate and nonprimate mammal species tested so far on this type of cognitive task, including chimpanzees, with regard to their learning speed in both the initial learning task and in the first reversal task, suggesting a high degree of behavioral flexibility and inhibitory control. Our findings support the notion that socio-ecological factors such as dietary specialization and social complexity foster advanced cognitive skills in primates.

Navigating the Global Supply Chain: Innovations and Challenges in DHL's Intermodal Transport Strategy

In the realm of global logistics, DHL stands out for its innovative approach to intermodal transport, seamlessly integrating various modes of transportation to optimize efficiency. This includes comprehensive services ranging from cargo inspection to customs clearance, all aimed at ensuring smooth cross-border solutions. However, DHL grapples with formidable challenges such as adverse weather conditions, traffic congestion, cargo safety concerns, and a shortage of skilled drivers. To address these challenges, the company is leveraging advanced technologies like predictive weather forecasting and electronic tracking systems. Moreover, strategic initiatives such as improving road transport strategies and adopting a Retain & Rehire approach to driver management are being implemented to enhance operational resilience. Furthermore, the integration of cutting-edge technologies such as 5G and Vehicle-to-Infrastructure (V2I) communication promises to revolutionize DHL's transportation and logistics landscape, ensuring secure, punctual, and efficient delivery of shipments in an increasingly complex global market.

The Effects of Different Drivers’ Steering Inputs on the Response of Heavy Ground Vehicles to Crosswind Disturbances

The general approach in the previous studies was to ignore the driver’s steering contribution to a vehicle while investigating the interactions between crosswind and vehicle. Therefore, the goal of this study is to find out how steering inputs by drivers affect a heavy-ground vehicle’s dynamic reaction to crosswinds. In the investigation, a two-way interaction between vehicle dynamics and aerodynamic simulations was employed. The steering inputs of drivers were modelled using a driver model taken from the previous literature that is able to reproduce the steering responses of a human driver. The study’s findings demonstrated that the steering inputs made by drivers significantly impacted how the vehicle responded to crosswinds. For instance, the greatest lateral displacement of the least skilled driver (Driver 1) was around 1.53 times the greatest lateral displacement of the most skilled driver (Driver 3) at the delay time of tδ,delay = 0.5 s in the steering input. Additionally, the maximum lateral displacement results of Driver 1 and Driver 3 at tδ,delay = 1.0 s became 1.39 and 1.56 times greater than their maximum lateral displacement results at tδ,delay = 0.5 s. Similarly, the total steering inputs of Driver 1 and Driver 3 at tδ,delay = 1.0 s were 1.4 and 2.2 times greater than their total steering inputs at tδ,delay = 0.5 s, respectively. In general, the results of a driver who is more skilled than Driver 1 (Driver 2) fall in between the respective results of Driver 1 and Driver 3. On the other hand, each driver’s total steering inputs at tδ,delay = 0.5 s were roughly the same as their total steering inputs at tδ,delay = 0 s. In all delay scenarios for the start of the driver’s steering inputs, the drivers’ steering inputs amplified the yaw moment applied to the vehicle. Meanwhile, they diminished the lateral force and roll moment.

A Human-Like Trajectory Planning Method on a Curve Based on the Driver Preview Mechanism

With the development of intelligent vehicle technology, many studies have been focused on developing human-like trajectory planning methods for automated driving systems. Although data-driven methods are widely used for human driver behavior learning, there have been fewer studies on realizing human-like trajectory planning by using the generation mechanism of driving behavior, especially under curve conditions, where the lane centerline has been denoted as a reference trajectory. In this paper, thirty-two skilled drivers were recruited to collect data under different curve conditions on a self-designed driver-in-the-loop system. The collected data are processed by dynamic time warping, trajectories with different lengths are warped and the abnormal data are removed. Based on the warped data, common characteristics and differences between left and right turning trajectories are compared and explored from the perspectives of drivers’ demand for turning performance and their visual attention mechanism. Then, by introducing the driver preview mechanism, two features with a strong ability to represent the generation mechanism of the driver’s curve driving behavior are introduced. Finally, the preview-based human-like trajectory planning model (PHTPM) is proposed, and it is verified and analyzed by comparative tests and generalizability tests. The results show that the introduction of the driver preview mechanism enables PHTPM to match the characteristics of skilled drivers accurately on left turnings and outperform them on right turnings.

Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions

Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety.

Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios

The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite direction (LTAP/OD) scenarios are subdivided into comfort, discomfort, extreme, and crash, and the boundaries of each status domain are quantified and visualized. First, real unprotected left turn road segments are chosen for the actual vehicle testing. Subjective passenger comfort evaluation results and objective motion state data of vehicles during the experiment are organized and analyzed by statistics. In addition, the pictorials are plotted to determine the comfort and extreme status domain boundaries based on motion state parameters. Second, based on the unprotected left turn kinematic analysis and modeling, as well as a skilled driver risk perception and operational model, the Safe Collision Plots (SCP) of conflicting vehicles in LTAP/OD scenarios are quantified and expressed as pictorial examples. By combining objective motion parameters and passenger experience, intuitively quantifying each driving status domain of vehicles can provide more fine-grained information for the design parameters of ADAS and autonomous vehicles and increase public trust and acceptance of them.

An examination of the relationship between financial performance and safety factors in the heavy trucking industry

Heavy trucks disproportionately contribute to road fatalities and injuries worldwide. Safety efforts to reduce these undesired outcomes have mostly focused on regulating overt driver behaviors such as speeding and fatigue, ignoring underlying systemic issues such as the financial performance of the company. Nevertheless, there is evidence that financial pressures may lead organizations to reduce safety investments and adopt payment policies that implicitly pressure drivers to drive faster and work longer hours. This paper examines the relationships between financial performance of organizations and crash influential factors, including driver employment type, driver payment method, driver skills level, type of load carried, technology adoption and vehicle acquisition. Data collected from an online survey of 69 Australian heavy trucking organizations was analyzed using the Correspondence Analysis methodology. Results showed a positive association between good financial performance and the adoption of safety-promoting policies, such as having skilled drivers, buying new vehicles or vehicles highly equipped with safety technologies. This implies that organizations’ financial performance in the trucking industry can be considered a safety performance indicator with a poor financial performance implying a risk of reducing safety investments. Results also showed that good financial performance is associated with carrying livestock, refrigerated or dangerous freight, selecting employee drivers, and paying drivers based on performance. Findings of this study have strong potential to improve truck safety by informing policy development, particularly on examining the inclusion of financial performance into the truck regulatory legislation.

GoonDAE: Denoising-Based Driver Assistance for Off-Road Teleoperation

Due to the limitations of autonomous driving technology, teleoperation is used extensively in hazardous environments such as military operations. However, the performance of teleoperated driving is primarily influenced by the driver's skill level. In other words, unskilled drivers need extensive training for teleoperation in harsh and unusual environments, such as off-road. In this letter, we propose GoonDAE, a novel denoising-based real-time driver assistance method that enables stable teleoperated off-road driving. We introduce a denoising autoencoder (DAE) based on a skip-connected long short-term memory (LSTM) to assist the unskilled driver control input through denoising. In this approach, it is assumed that the control input of an unskilled driver is equivalent to that of a skilled driver with noise. We train GoonDAE using the skilled driver control inputs and sensor data collected from our simulated off-road driving environment. Our experiments in the simulated off-road environment show that GoonDAE significantly improves the driving stability of unskilled drivers.

Data Mining Approach for the Identification of the Intelligent Gear Decision Strategy for the Stepped Automatic Transmission

The primary goal of automatic transmission vehicle design and optimization is to ensure that drivers can operate automatic transmission vehicles with the same driving experience as skilled drivers driving manual transmission vehicles. However, the existing gear decision strategies of stepped automatic transmission vehicles have poor adaptability to different driving intentions and complex driving environments. When operating manual transmission vehicles, skilled drivers can make intelligent gear decision to satisfy their driving intentions and adapt to different driving environments. Therefore, this study's objective is to establish the design and formulation process for the identification of the intelligent gear decision strategy for the stepped automatic transmission that adapts to different driving intentions and complex driving environments. This is achieved using the data mining method to identify skilled drivers' gear decision strategy from their driving data. To this end, the driving data of skilled drivers operating manual transmission vehicles are collected. The shift boundary points of each gear under the target driving condition are obtained through data preprocessing, data cleaning, outlier processing, and boundary points extraction. Thereafter, the gear decision strategy of skilled drivers is established by fitting the shift boundary points. Taking the flat-straight road condition as an example, the gear decision strategy is established and experimental comparisons are made to verify the effectiveness of proposed gear decision design method.

Cerebellum neuropathology and motor skill deficits in fragile X syndrome

Fragile X syndrome (FXS) is a leading form of inherited intellectual disability and single-gene cause of autism spectrum disorder (ASD) and is characterized by core deficits in cognitive flexibility, sensory sensitivity, emotion, and social interactions. Motor deficits are a shared feature of FXS and autism. The cerebellum has emerged as one of the target brain areas affected by neurodevelopmental diseases. Alterations in the cerebellar structure, circuits, and function may be the key drivers of impaired fine and gross motor skills in FXS and fragile X-associated tremor/ataxia syndrome (FXTAS). In this review, we briefly examined recent findings in FXS and present a discussion on the literature supporting motor skill deficits in FXS. Subsequently, we focused on neuropathological alterations in the cerebellum in FXS and FXTAS. We highlight studies that have directly examined the function of fragile X mental retardation protein and related epigenetic variations in the cerebellum. Overall, we obtained considerable supporting evidence for the hypothesis that cerebellar dysfunction is evident in FXS and FXTAS; however, compared with studies on other ASD models, studies on motor skills related to fragile X disorders are particularly rare and inconclusive. Hence, future research should address FXS-related motor and behavioral trajectories and examine the underlying mechanisms at both the cell and circuit levels.

Research on human-like lane-changing control of intelligent vehicles based on extension goodness evaluation

In order to improve the trajectory tracking accuracy and passenger comfort for intelligent vehicle lane-changing at medium and high speed, this paper proposes a new control algorithm based on extension evaluation system for intelligent vehicle lane-changing. In this research, firstly, a vehicle-road dynamics model has been developed. In addition, the actual driving trajectory data of skilled drivers are collected by driving simulator, and the lane-changing path planning model is established based on generalized regression neural network (GRNN). Last, in order to improve the tracking ability to the planning path, the trajectory tracking controller is established. A two-layer structure of lateral trajectory tracking control system is designed, in which the upper layer includes two algorithms: (1) PID feedback control based on preview deviation; (2) PID feedforward-feedback control based on the curvature of the path planning by skilled drivers. While the extension goodness evaluation is introduced in the lower layer which is used to evaluate two controllers in upper layer. According to the real-time state of vehicle-road system, the controller output with higher goodness is selected. Finally, the simulation results verify that comparing with the conventional single PID feedback control, the extension goodness evaluation control can effectively improve the passenger comfort while ensuring tracking accuracy.

Novel use of a virtual driving assessment to classify driver skill at the time of licensure

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

Intelligent Vehicle Moving Trajectory Prediction Based on Residual Attention Network

Skilled drivers have the driving behavioral characteristic of pre-sighted following, and similarly intelligent vehicles need accurate prediction of future trajectories. The LSTM (Long Short-Term Memory) is a common model of trajectory prediction. The existing LSTM models pay less attention to the interactions between the target and the surrounding vehicles. Furthermore, the impacts on future trajectories of the target vehicle have also barely been a focus of the current models. On these bases, a Residual Attention-based Long Short-Term Memory (RA-LSTM) model was proposed, an interaction tensor based on the surroundings of the target vehicle at the predictive moments was constructed and the weight coefficients of the interaction tensor for the surrounding vehicles relative to the target vehicle were calculated and re-programmed in this study. The proposed RA-LSTM model can implicitly represent the different degrees of influence of the surrounding vehicles on the target vehicle; the probability distributions of the future trajectory coordinates of the target vehicle is predicted based on the extracted interaction features. The RA-LSTM model was tested and verified in multiple scenarios by using the NGSIM (next generation simulation) public dataset, and the results showed that the prediction accuracy of the proposed model is significantly improved compared with the current LSTM models.

Implementation of the Human‐Like Lane Changing Driver Model Based on Bi‐LSTM

If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human‐like lane‐changing model of an autonomous vehicle. The lane‐changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC‐based driver model with the steering wheel angle of human drivers, we found that the MPC‐based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human‐like lane‐changing driving model based on Bi‐LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi‐LSTM‐based human‐like lane‐changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane‐changing conditions.

Has Excel Become a “Golden Hammer”: The Paradox of Data Analytics in SME Clusters

ABSTRACTDemand for data analysis skills in the accounting profession is well-documented and necessarily informs accounting curriculum and pedagogy. This empirical survey study focuses on small- and medium-sized entities (SMEs). SME cluster research suggests different adoption rates for technology between this segment and large firms. Investigating SME demands for skills and abilities of new job entrants differs from the large organizations that served as the original drivers of analytic skills and technology recommendations. Findings in this study suggest SMEs continue performing their leading accounting tasks using Excel spreadsheets and lag in adoption of data analytics technology. SMEs are a significant business sector in the economy, competing with large firms but with fewer resources. The findings presented here may inform educators in SME markets regarding SME firms' need for a unique skillset from new hires.

Language and ethnobiological skills decline precipitously in Papua New Guinea, the world’s most linguistically diverse nation

Papua New Guinea is home to >10% of the world's languages and rich and varied biocultural knowledge, but the future of this diversity remains unclear. We measured language skills of 6,190 students speaking 392 languages (5.5% of the global total) and modeled their future trends using individual-level variables characterizing family language use, socioeconomic conditions, students' skills, and language traits. This approach showed that only 58% of the students, compared to 91% of their parents, were fluent in indigenous languages, while the trends in key drivers of language skills (language use at home, proportion of mixed-language families, urbanization, students' traditional skills) predicted accelerating decline of fluency to an estimated 26% in the next generation of students. Ethnobiological knowledge declined in close parallel with language skills. Varied medicinal plant uses known to the students speaking indigenous languages are replaced by a few, mostly nonnative species for the students speaking English or Tok Pisin, the national lingua franca. Most (88%) students want to teach indigenous language to their children. While crucial for keeping languages alive, this intention faces powerful external pressures as key factors (education, cash economy, road networks, and urbanization) associated with language attrition are valued in contemporary society.

Blaming automated vehicles in difficult situations

SummaryAutomated vehicles (AVs) have made huge strides toward large-scale deployment. Despite this progress, AVs continue to make mistakes, some resulting in death. Although some mistakes are avoidable, others are hard to avoid even by highly skilled drivers. As these mistakes continue to shape attitudes toward AVs, we need to understand whether people differentiate between them. We ask the following two questions. When an AV makes a mistake, does the perceived difficulty or novelty of the situation predict blame attributed to it? How does that blame attribution compare to a human driving a car? Through two studies, we find that the amount of blame people attribute to AVs and human drivers is sensitive to situation difficulty. However, while some situations could be more difficult for AVs and others for human drivers, people blamed AVs more, regardless. Our results provide novel insights in understanding psychological barriers influencing the public's view of AVs.