scholarly journals Analyzing Driver Drowsiness: From Causes to Effects

2020 ◽  
Vol 12 (5) ◽  
pp. 1971 ◽  
Author(s):  
Sónia Soares ◽  
Tiago Monteiro ◽  
António Lobo ◽  
António Couto ◽  
Liliana Cunha ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Driving Performance ◽  
Physiological Characteristic ◽  
Point Of View ◽  
Recent Event ◽  
Safety Issues ◽  
Driver Drowsiness ◽  
Driving Time ◽  
Karolinska Sleepiness Scale

Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.

scholarly journals An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing

2020 ◽  
Vol 10 (8) ◽  
pp. 2890
Author(s):  
Jongseong Gwak ◽  
Akinari Hirao ◽  
Motoki Shino
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Driving Simulator ◽  
Detection System ◽  
Driving Performance ◽  
Machine Learning Algorithms ◽  
Drowsy Driving ◽  
Physiological Indicators ◽  
Driver Drowsiness ◽  
Behavioral Indices

Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.

scholarly journals Driving Performance Evaluation Correlated to Age and Visual Acuities Based on VR Technologies

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sooncheon Hwang ◽  
Sunhoon Kim ◽  
Dongmin Lee
Keyword(s):  
Visual Acuity ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Driving Performance ◽  
Present System ◽  
The South ◽  
Dynamic Visual Acuity ◽  
South Korean ◽  
Driving Behaviors ◽  
License System

There is currently much debate regarding the effectiveness of the driver license system in South Korea, due to the numerous traffic crashes caused by drivers who are suspected of having insufficient physical and mental abilities. Through the present system, it is quite difficult to identify such drivers indirectly through physical tests, such as visual acuity tests, since the correlation of such results with driving performance remains unclear. The objective of this study was to investigate the relationship between driving performance and visual acuities for improving the South Korean driver license system. In this study, two investigations were conducted: static and dynamic visual acuity examinations and driving performance tests based on a virtual reality (VR) system. The driving performance was evaluated with a driving simulator, based on driving behaviors in different experimental scenarios, including daytime and nighttime driving on a rural highway, and unexpected incident situations. Here, we produce statistically significant evidence that reduced visual acuity impairs driving performance, and driving behaviors differ significantly among groups with different vision capabilities, especially dynamic vision. Visual acuities, typically dynamic visual acuity, greatly influenced driving behavior, as measured by the standard deviation of speeds and vehicle LPs, and this was especially notable in curved road segments in daytime experiment. These experimental results revealed that the driving performance of participants with impaired dynamic visual acuity was deficient and unsafe. This confirmed that dynamic visual acuity levels are significant determinants of driving behavior, and they well explain driver performance levels. These findings suggest that the South Korean driver license system should include a test of dynamic visual acuity to create better and safer driving.

scholarly journals Glaucomatous visual fields and neurocognitive function are independently associated with poor lane maintenance during driving simulation

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
David E. Anderson ◽  
John P. Bader ◽  
Emily A. Boes ◽  
Meghal Gagrani ◽  
Lynette M. Smith ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driving Simulation ◽  
Sensor Data ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Neurocognitive Performance ◽  
Loss Severity

Abstract Background Driving simulators are a safe alternative to on-road vehicles for studying driving behavior in glaucoma drivers. Visual field (VF) loss severity is associated with higher driving simulator crash risk, though mechanisms explaining this relationship remain unknown. Furthermore, associations between driving behavior and neurocognitive performance in glaucoma are unexplored. Here, we evaluated the hypothesis that VF loss severity and neurocognitive performance interact to influence simulated vehicle control in glaucoma drivers. Methods Glaucoma patients (n = 25) and suspects (n = 18) were recruited into the study. All had > 20/40 corrected visual acuity in each eye and were experienced field takers with at least three stable (reliability > 20%) fields over the last 2 years. Diagnosis of neurological disorder or cognitive impairment were exclusion criteria. Binocular VFs were derived from monocular Humphrey VFs to estimate a binocular VF index (OU-VFI). Montreal Cognitive Assessment (MoCA) was administered to assess global and sub-domain neurocognitive performance. National Eye Institute Visual Function Questionnaire (NEI-VFQ) was administered to assess peripheral vision and driving difficulties sub-scores. Driving performance was evaluated using a driving simulator with a 290° panoramic field of view constructed around a full-sized automotive cab. Vehicle control metrics, such as lateral acceleration variability and steering wheel variability, were calculated from vehicle sensor data while patients drove on a straight two-lane rural road. Linear mixed models were constructed to evaluate associations between driving performance and clinical characteristics. Results Patients were 9.5 years older than suspects (p = 0.015). OU-VFI in the glaucoma group ranged from 24 to 98% (85.6 ± 18.3; M ± SD). OU-VFI (p = .0066) was associated with MoCA total (p = .0066) and visuo-spatial and executive function sub-domain scores (p = .012). During driving simulation, patients showed greater steering wheel variability (p = 0.0001) and lateral acceleration variability (p < .0001) relative to suspects. Greater steering wheel variability was independently associated with OU-VFI (p = .0069), MoCA total scores (p = 0.028), and VFQ driving sub-scores (p = 0.0087), but not age (p = 0.61). Conclusions Poor vehicle control was independently associated with greater VF loss and worse neurocognitive performance, suggesting both factors contribute to information processing models of driving performance in glaucoma. Future research must demonstrate the external validity of current findings to on-road performance in glaucoma.

scholarly journals Aging: Older Adults’ Driving Behavior Using Longitudinal and Lateral Warning Systems

2019 ◽  
Vol 62 (2) ◽  
pp. 229-248 ◽  
Author(s):  
Dustin J. Souders ◽  
Neil Charness ◽  
Nelson A. Roque ◽  
Hellen Pham
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Driving Performance ◽  
Older Drivers ◽  
Critical Event ◽  
Warning Systems ◽  
Driver Assistance Systems ◽  
Subjective Workload ◽  
Concurrent Use ◽  
Forward Collision Warning

Objective This study assessed older drivers’ driving behavior when using longitudinal and lateral vehicle warning systems together. Background Advanced driver assistance systems (ADAS) can benefit drivers of all ages. Previous research with younger to middle-aged samples suggests that safety benefits are not necessarily additive with additional ADAS. Increases in following distance associated with the use of forward collision warning (FCW) decreased when drivers also used lane departure warning (LDW), likely due to attending to the LDW more than the FCW. Method The current study used a driving simulator to provide 128 older drivers experience with FCW and/or LDW system(s) during a ~25-min drive to gauge their usage’s effects on driving performance and subjective workload. Results There were no significant differences found in headway distance between older drivers who used different combinations of FCW and LDW systems, but those who used an FCW system showed significantly longer time-to-collision (TTC) when approaching the critical event than those who did not. Users of LDW systems did not show reductions in standard deviation of lane position. Analyses of subjective workload measures showed no significant differences between conditions. Conclusion Findings suggest that FCW could increase older drivers’ TTC over the course of a drive. Contrary to previous findings in younger samples, concurrent use of FCW and LDW systems did not adversely affect older drivers’ longitudinal driving performance and subjective workload. Application Potential applications of this research include the assessment of older drivers’ use of vehicle warning systems and their effects on subjective workload.

Effects of Gesture-Based Interaction on Driving Behavior: A Driving Simulator Study Using the Projection-Based Vehicle-in-the-Loop

2020 ◽  
pp. 001872082094328
Author(s):  
Lisa Graichen ◽  
Matthias Graichen ◽  
Josef F. Krems
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Negative Impact ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driver Distraction ◽  
Safe Alternative ◽  
Visual Distraction ◽  
Performance Effects

Objective We observe the driving performance effects of gesture-based interaction (GBI) versus touch-based interaction (TBI) for in-vehicle information systems (IVISs). Background As a contributing factor to a number of traffic accidents, driver distraction is a significant problem for traffic safety. More specifically, visual distraction has a strong negative impact on driving performance and risk perception. Thus, the implementation of new interaction systems that use midair gestures to encourage glance-free interactions could reduce visual distraction among drivers. Methods In this experiment, participants drove a projection-based Vehicle-in-the-Loop. The projection-based technology combines a visual simulation with kinesthetic, vestibular, and auditory feedback from a car on a test track. While driving, participants used GBI or TBI to perform IVIS tasks. To investigate driving behavior related to critical driving situations and car-following maneuvers, vehicle data based upon longitudinal and lateral driving were collected. Results Participants reacted faster to critical driving situations when using GBI compared to TBI. For drivers using TBI, steering performance decreased and time headway to a preceding vehicle was higher. Conclusion Gestures provide a safe alternative to in-vehicle interactions. Moreover, GBI has fewer effects on driver distraction than TBI. Application Potential applications of this research include all in-vehicle interaction systems used by drivers.

scholarly journals Assessing the Influence of Adverse Weather on Traffic Flow Characteristics Using a Driving Simulator and VISSIM

2019 ◽  
Vol 11 (3) ◽  
pp. 830 ◽  
Author(s):  
Chen Chen ◽  
Xiaohua Zhao ◽  
Hao Liu ◽  
Guichao Ren ◽  
Yunlong Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Driving Simulator ◽  
Traffic Simulation ◽  
Flow Characteristics ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Point Of View ◽  
Microscopic Traffic Simulation ◽  
Adverse Weather ◽  
Traffic Flow Characteristics

The occurrence of adverse weather exacerbates traffic flow conditions, often leading to severe traffic congestions. Many studies have been conducted based on field-collected data to obtain the effects of weather on traffic flow characteristics. However, there is a limitation for filed data-based studies, in that weather conditions and traffic conditions are both noncontrollable and nonrepeatable, making it difficult to comprehensively assess the influence of weather conditions, especially the rare extreme weather conditions, on traffic flow characteristics. This paper proposes to assess these effects with the combination of driving simulator and traffic simulation. A driving simulator can collect driving behavior by conducting weather-related driving simulation experiments, while a microscopic traffic simulation program can evaluate the changes in traffic flow characteristics by inputting driving behavior parameters coming from the driving simulator. The proposed method can overcome the limitation of the field data-based approach. In this paper, the structure of the assessment platform is introduced at first. Then a verification experiment is conducted to measure the influences of adverse weather conditions on traffic flow characteristics. The verification experiment results show that the influences of adverse weather on traffic flow characteristics have consistent tendencies with outcomes from previous research and demonstrate that the method is practicable for the analysis of the influence of weather on traffic flow characteristics. This paper provides a practical way to analyze the influence of weather on traffic flow from driving behavior’s point of view.

Effects of Individual Differences, Attention, and Memory Deficits on Driver Distraction

2020 ◽  
Vol 64 (1) ◽  
pp. 1196-1201
Author(s):  
Alejandro A. Arca ◽  
Kaitlin M. Stanford ◽  
Mustapha Mouloua
Keyword(s):  
Individual Differences ◽  
Driving Simulator ◽  
Memory Deficits ◽  
Driving Performance ◽  
Driver Distraction ◽  
Phone Call ◽  
Traffic Density ◽  
Attention And Memory ◽  
Adhd Diagnosis ◽  
Simulated Driving

The current study was designed to empirically examine the effects of individual differences in attention and memory deficits on driver distraction. Forty-eight participants consisting of 37 non-ADHD and 11 ADHD drivers were tested in a medium fidelity GE-ISIM driving simulator. All participants took part in a series of simulated driving scenarios involving both high and low traffic conditions in conjunction with completing a 20-Questions task either by text- message or phone-call. Measures of UFOV, simulated driving, heart rate variability, and subjective (NASA TLX) workload performance were recorded for each of the experimental tasks. It was hypothesized that ADHD diagnosis, type of cellular distraction, and traffic density would affect driving performance as measured by driving performance, workload assessment, and physiological measures. Preliminary results indicated that ADHD diagnosis, type of cellular distraction, and traffic density affected the performance of the secondary task. These results provide further evidence for the deleterious effects of cellphone use on driver distraction, especially for drivers who are diagnosed with attention-deficit and memory capacity deficits. Theoretical and practical implications are discussed, and directions for future research are also presented.

scholarly journals Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Thierry Bellet ◽  
Aurélie Banet ◽  
Marie Petiot ◽  
Bertrand Richard ◽  
Joshua Quick
Keyword(s):  
Artificial Intelligence ◽  
Adaptive Management ◽  
Driving Simulator ◽  
End Users ◽  
Point Of View ◽  
Human Centered Design ◽  
Perceived Utility ◽  
New Type ◽  
Future Potential ◽  
Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.

scholarly journals Driving Performance and Its Correlation with Neuropsychological Tests in Senior Drivers with Cognitive Impairment in Japan

2021 ◽  
Vol 79 (4) ◽  
pp. 1575-1587
Author(s):  
Zhouyuan Peng ◽  
Hiroyuki Nishimoto ◽  
Ayae Kinoshita
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Functions ◽  
Neuropsychological Tests ◽  
Driving Simulator ◽  
Driving Performance ◽  
Impaired Driving ◽  
Dementia Patients ◽  
Neuropsychological Factors ◽  
Senior Drivers

Background: With the rapid aging of the population, the issue of driving by dementia patients has been causing increasing concern worldwide. Objective: To investigate the driving difficulties faced by senior drivers with cognitive impairment and identify the specific neuropsychological tests that can reflect specific domains of driving maneuvers. Methods: Senior drivers with cognitive impairment were investigated. Neuropsychological tests and a questionnaire on demographic and driving characteristics were administered. Driving simulator tests were used to quantify participants’ driving errors in various domains of driving. Results: Of the 47 participants, 23 current drivers, though they had better cognitive functions than 24 retired drivers, were found to have impaired driving performance in the domains of Reaction, Starting and stopping, Signaling, and Overall (wayfinding and accidents). The parameters of Reaction were significantly related to the diagnosis, and the scores of MMSE, TMT-A, and TMT-B. As regards details of the driving errors, “Sudden braking” was associated with the scores of MMSE (ρ= –0.707, p < 0.01), BDT (ρ= –0.560, p < 0.05), and ADAS (ρ= 0.758, p < 0.01), “Forgetting to use turn signals” with the TMT-B score (ρ= 0.608, p < 0.05), “Centerline crossings” with the scores of MMSE (ρ= –0.582, p < 0.05) and ADAS (ρ= 0.538, p < 0.05), and “Going the wrong way” was correlated with the score of CDT (ρ= –0.624, p < 0.01). Conclusion: Different neuropsychological factors serve as predictors of different specific driving maneuvers segmented from driving performance.

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