Study on the Changes of EEG Signal and Driving Behavior Based on the Driving Simulator

2017 ◽  
pp. 409-420
Author(s):  
Guangyin Han ◽  
Liu Yang
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Eeg Signal ◽  
Behavior Based

Advance Information on the Road: A Simulator Study of the Effect of Road Markings

1976 ◽  
Vol 18 (6) ◽  
pp. 521-532 ◽  
Author(s):  
Harald Witt ◽  
Carl G. Hoyos
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Speed Profile ◽  
The Road ◽  
Positive Effects ◽  
Road Signs ◽  
Advance Information ◽  
Simulator Study ◽  
On The Road ◽  
Accident Statistics

Accident statistics and studies of driving behavior have shown repeatedly that curved roads are hazardous. It was hypothesized that the safety of curves could be improved by indicating in advance the course of the road in a more effective way than do traditional road signs. A code of sequences of stripes put on right edge of the pavement was developed to indicate to the driver the radius of the curve ahead. The main characteristic of this code was the frequency of transitions from code elements to gaps between elements. The effect of these markings was investigated on a driving simulator. Twelve subjects drove on simulated roads of different curvature and with different placement of the code in the approach zone. Some positive effects of the advance information could be observed. The subjects drove more steadily, more precisely, and with a more suitable speed profile.

scholarly journals Investigating the impact of Innovative Treatments on Driving Behavior at School Bus Stops: A Driving Simulator Study

2021 ◽  
Author(s):  
Mustafa Suhail Almallah ◽  
Shabna Sayed Mohammed ◽  
Qinaat Hussain ◽  
Wael K. M. Alhajyaseen
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
School Bus ◽  
School Buses ◽  
Bus Stop ◽  
Typical Condition ◽  
Simulator Study ◽  
And Control ◽  
The Impact ◽  
Traveling Speed

The illegal overtaking/crossing of stopped school buses has been identified as one of the leading causes of students’ injuries and fatalities. The likelihood of students in getting involved in a school bus-related crash increases during loading/unloading. The main objective of this driving simulator study was to study the effectiveness of different treatments in improving students’ safety by reducing the illegal overtaking/crossing of stopped school buses. Treatments used in this research are LED, Road Narrowing and Red Pavement. All proposed treatments were compared with the control condition (i.e., typical condition in the State of Qatar). Seventy-two subjects with valid Qatari driving license were invited to participate in this study. Each subject was exposed to three situations (i.e., Situation 1: the school bus is stopped in the same traveling direction, Situation 2: the school bus is stopped in the opposite traveling direction, Situation 3: the school bus is not present at the bus stop). Results showed that LED and Road Narrowing treatments were effective in reducing the illegal overtaking/crossing of stopped school buses. Moreover, the stopping behavior for drivers in LED and Road Narrowing was more consistent compared to the Red Pavement and control conditions. Finally, LED and Road Narrowing treatments motivated drivers to reduce their traveling speed by 5.16 km/h and 5.11 km/h, respectively, even with the absence of the school bus. Taking into account the results from this study, we recommend the proposed LED and Road Narrowing as potentially effective treatments to improve students’ safety at school bus stop locations.

Driving Behavior Analysis and Speed Limit Optimization with the Driving Simulator

2021 ◽  
Author(s):  
Fan Wang ◽  
Fan Yang ◽  
Wei Yang ◽  
Huachun Tan ◽  
Bin Ran
Keyword(s):  
Behavior Analysis ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Speed Limit

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 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.

Multi-agent and driving behavior based rear-end collision alarm modeling and simulating

2010 ◽  
Vol 18 (8) ◽  
pp. 1092-1103 ◽  
Author(s):  
Jun Liang ◽  
Long Chen ◽  
Xian-yi Cheng ◽  
Xian-bo Chen
Keyword(s):  
Driving Behavior ◽  
Multi Agent ◽  
Behavior Based
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