Comparison between elderly and young drivers' performances on a driving simulator and self-assessment of their driving attitudes and mastery

Comparative analysis of mental workload and performance between young and elderly drivers: Implications for road safety and age-related driving challenges

BACKGROUND

Driving represents a multifaceted cognitive endeavor, demanding heightened vigilance and swift responses. Considering the high statistics of driving accidents and heavy loads, as well as the effect of the driver's age on the occurrence of accidents, it is important to investigate these factors to reduce accidents.

OBJECTIVE

This study investigates the impact of mental workload on the performance of young and older drivers in a dynamic driving scenario to compare cognitive performance, workload perception, and driving outcomes between the two age groups.

METHODS

Cognitive tests including the Stroop test, Continuous Performance test, and Focused Attention test were conducted, alongside the use of the DALI questionnaire to measure workload levels. Participants encompassed twenty male drivers, divided into two age groups: 20 to 35 years and 55 to 70 years, with varying years of driving experience. The study entailed a dynamic driving scenario involving a designated route in Tehran, Iran.

RESULTS

Results exhibited differences in workload scores between the age groups, particularly in dimensions such as visual demand, auditory demand, attention, and interference. Older drivers demonstrated heightened cognitive and physical demands during driving, implying a greater need for attention and cognitive effort.

CONCLUSION

The findings of this study indicated that navigating through congested roads and dense urban traffic significantly elevates the mental workload for drivers, consequently impacting their cognitive functioning. Given the critical need for attention in driving, this heightened workload can manifest as increased fatigue, increasing stress levels, and diminished concentration, all of which substantially raise the risk of vehicular accidents. Furthermore, the study highlighted a particular concern for older drivers, whose diminished cognitive capacities further raise their vulnerability to accidents under such demanding driving conditions.

Analysis of coping capacities and cognitive biases of novice drivers-A questionnaire-based study

Coping capacity is a key aspect of driver-vehicle interaction when drivers observe and make decisions, and is of great importance for drivers. However, different drivers have different self-cognition and assess their driving abilities differently, especially for novice drivers. Based on questionnaire data, this study has investigated the coping capacities of drivers in both static environments and dynamic environments. With the ANOVA analysis method and the structural equation model (SEM), this study has verified the effects of gender and driving factors (driving years, driving frequency, driving time) on drivers' coping capacities based on drivers' self-assessment scores and mutual assessment scores. Drivers' self-assessment scores show significant effects of all factors on drivers' coping capacities, and drivers' mutual assessment scores show significant effects of all factors, excluding driving time, on drivers' coping capacities. Also, it has been found that all drivers in the driving year group have cognitive biases. It seems that first-year drivers are always overconfident with their driving skills, while drivers with a driving experience of more than three years usually score driving skills of themselves and other drivers most conservatively. With increased exposure to various traffic conditions, experienced drivers are more aware of their limitations in dealing with complex traffic situations, while novice drivers do not know their lack of capability to properly respond to any unexpected situation they could encounter.

Simulator Driving Abilities, Executive Functions, and Adaptive Behavior Among Adolescents With Complex Attention Deficit Hyperactivity Disorder

OBJECTIVE

To examines the driving skills, executive functions, and adaptive behavior of adolescents diagnosed with Attention-deficit/hyperactivity disorder (ADHD) combined with emotional difficulties and/or learning disabilities (complex ADHD), compared to adolescents without ADHD.

METHOD

A cross-sectional study including 30 adolescents with complex ADHD and 33 adolescents without ADHD aged 15 to 18 years. Driving skills on a simulator, executive functions, and adaptive behavior were evaluated.

RESULTS

Adolescents without ADHD demonstrated significantly higher total scores on the driving simulator, indicating better driving performance compared to adolescents with complex ADHD. In the complex ADHD group, after controlling for different Conners Comprehensive Behavior Rating Scale-Self Report scales, driving simulator scores were partially correlated with executive functions.

CONCLUSION

These findings emphasize the unique needs of adolescents with complex ADHD. Given the significance of driving in today's society, it is crucial to consider the development of intervention programs to support this population's driving skills.

Why do older drivers self-regulate: Psychological factors influencing self-regulation in a Chinese sample

INTRODUCTION

As China has recently lifted the age limit for getting access to driving licenses, older drivers' safety issues have received wide attention. Driving self-regulation can be regarded as an adjustment strategy for older adults to reduce risks and extend driving lives. Studies abroad mainly stress the three levels and influencing factors of driving self-regulation. China has a long history with a unique cultural background and social reality and relevant research are still in the initial stage.

METHOD

This study applied the extended Theory of Planned Behavior (TPB) to explore the psychological factors that affected self-regulation of older drivers. 317 participants mainly from Beijing urban area completed the questionnaires including demographic information and extend TPB items.

RESULTS

Bivariate correlation analysis showed that self-regulation was negatively correlated with the amount of driving experience (days per week and mileages per month) in a significant way. And so was the number of family-owned motor vehicles. Exploratory factor analysis indicated that the extended TPB questionnaire was reliable and effective for measuring self-regulation. The proposed Structural Equation Model (SEM) explained 73.673% of the variance in self-regulation intention. Attitude (0.50) had the strongest influence among all variables on intention. Subjective norms (0.28), perceived behavior control (0.27), and alternative traffic quality (0.20) significantly influenced intention. Intention (0.34) and physical condition (0.22) imposed significant effect on self-regulation behavior. Practical applications: Feasible suggestions were put forward that contribute to self-regulate reasonably. This study helps to better understand the nature of self-regulation behaviors and provides a theoretical basis for formulating scientific intervention measures. On the transportation side, people from all communities of society should care for and support older drivers.

Simultaneous Eye Blink Characterization and Elimination from Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection

OBJECTIVE

Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of drivers cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data.

METHODS

Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features.

MAIN RESULTS

When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p<0.05).

SIGNIFICANCE

This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.

Functional Assessment of Driving Capacity of Older Drivers Compared with Non-older Drivers Using Clinical Evaluations and Driving Simulations

Background

This study used various tools, including a self-assessment questionnaire, the Clinical Assessment of Driver-Related Skills (CADReS), and a driving simulation, to discriminate between older and non-older drivers.

Methods

We evaluated driving habits, driving-related health behaviors, and morbidities using a self-assessment questionnaire and examined visual, motor, and cognitive functions using the CADReS and a vehicle simulator of four junction scenarios that are typical of accidents involving older drivers. The areas under the receiver operating characteristic curves (AUCs) were calculated to compare the age-related discriminating ability of these tools between older (≥65 years) and non-older participants.

Results

Thirty of the 40 participants (75%) were older. Older drivers were slower than non-older drivers according to the rapid walking pace (8.0 vs. 6.1 seconds), and their cognitive function was poorer based on the trail-making test (117 vs. 51 seconds). While driving on the vehicle simulator, the rate of violating traffic rules was higher and the maximal velocity was slower in the older group than in the non-older group. The AUC values for CADReS and driving simulation outcomes ranged from 0.59 to 0.95, while the rapid walking pace, trail-making test, and velocity of the left turn at an acute junction in the dark showed high discriminatory power (AUC>0.9).Conclusions: The rapid walking pace and trail-making test in CADReS, as well as the driving simulation, were useful tools to discriminate between older and non-older drivers.

Blueprint for a Simulation Framework to Increase Driver Training Safety in North America: Case Study

Despite numerous recent advances in the classroom and in-vehicle driver training and education over the last quarter-century, traffic accidents remain a leading cause of mortality for young adults—particularly, those between the ages of 16 and 19. Obviously, despite recent advances in conventional driver training (e.g., classroom, in-vehicle, Graduated Driver Licensing programs), this remains a critical public safety and public health concern. As advanced vehicle technologies continue to evolve, so too does the unintended potential for mechanical, visual, and/or cognitive driver distraction and adverse safety events on national highways. For these reasons, a physics-based modeling and high-fidelity simulation have great potential to serve as a critical supplementary component of a near-future teen-driver training framework. Here, a case study is presented that examines the specification, development, and deployment of a “blueprint” for a simulation framework intended to increase driver training safety in North America. A multi-measure assessment of simulated driver performance was developed and instituted, including quantitative (e.g., simulator-measured), qualitative (e.g., evaluator-observed), and self-report metrics. Preliminary findings are presented, along with a summary of novel contributions through the deployment of the training framework, as well as planned improvements and suggestions for future directions.

In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors

This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use.