Human Error in Transportation Systems

2012 ◽  
pp. 163-180
Keyword(s):  
Human Error ◽  
Transportation Systems

scholarly journals Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammad Khashayarfard ◽  
Habibollah Nassiri
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Safety Assessment ◽  
Human Error ◽  
Transportation Systems ◽  
Text Messages ◽  
Assessment Model ◽  
Driving Simulators ◽  
Time To Collision ◽  
Unsignalized Intersections

Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.

scholarly journals Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

2020 ◽  
Author(s):  
Arafat Al-Dweik ◽  
Reza Mohammadi Tamanani ◽  
Radu Muresan
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Human Error ◽  
Transportation Systems ◽  
Practical Applications ◽  
Facial Region ◽  
Learning Techniques ◽  
Haar Cascade ◽  
And Performance ◽  
Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

scholarly journals Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

2020 ◽  
Author(s):  
Arafat Al-Dweik ◽  
Reza Mohammadi Tamanani ◽  
Radu Muresan
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Human Error ◽  
Transportation Systems ◽  
Practical Applications ◽  
Facial Region ◽  
Learning Techniques ◽  
Haar Cascade ◽  
And Performance ◽  
Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

scholarly journals Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

2020 ◽  
Author(s):  
Arafat Al-Dweik ◽  
Reza Mohammadi Tamanani ◽  
Radu Muresan
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Human Error ◽  
Transportation Systems ◽  
Practical Applications ◽  
Facial Region ◽  
Learning Techniques ◽  
Haar Cascade ◽  
And Performance ◽  
Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

An Alternative Method for Identifying a Failed Engine in Twin-Engine Propeller Aircraft

2020 ◽  
Vol 10 (2) ◽  
pp. 103-111
Author(s):  
Andrey K. Babin ◽  
Andrew R. Dattel ◽  
Margaret F. Klemm
Keyword(s):  
Human Error ◽  
Flight Simulator ◽  
Traditional Group ◽  
Aircraft Accidents ◽  
Alternative Method ◽  
Indicator Group ◽  
Group A ◽  
The Dead ◽  
Engine Failure ◽  
And Performance

Abstract. Twin-engine propeller aircraft accidents occur due to mechanical reasons as well as human error, such as misidentifying a failed engine. This paper proposes a visual indicator as an alternative method to the dead leg–dead engine procedure to identify a failed engine. In total, 50 pilots without a multi-engine rating were randomly assigned to a traditional (dead leg–dead engine) or an alternative (visual indicator) group. Participants performed three takeoffs in a flight simulator with a simulated engine failure after rotation. Participants in the alternative group identified the failed engine faster than the traditional group. A visual indicator may improve pilot accuracy and performance during engine-out emergencies and is recommended as a possible alternative for twin-engine propeller aircraft.

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