AIMC Topic: Accidents, Traffic

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Variance-based global sensitivity analysis for rear-end crash investigation using deep learning.

Accident; analysis and prevention Dec 8, 2021
Traffic accidents are rare events with inconsistent spatial and temporal dimensions; thus, accident injury severity (INJ-S) analysis faces a significant challenge in its classification and data stability. While classical statistical models have limit...
Accidents, Traffic Machine Learning Deep Learning Logistic Models Models, Statistical Humans
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Finding and understanding pedal misapplication crashes using a deep learning natural language model.

Traffic injury prevention Dec 7, 2021
OBJECTIVE: The objective of this study was to develop a system which used the BERT natural language understanding model to identify pedal misapplication (PM) crashes from their crash narratives and validate the accuracy of the system.
Deep Learning Algorithms North Carolina Humans Accidents, Traffic Language
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A high-resolution trajectory data driven method for real-time evaluation of traffic safety.

Accident; analysis and prevention Dec 2, 2021
Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are com...
Machine Learning Research Design Humans Accidents, Traffic Safety Logistic Models Support Vector Machine
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Vision-Based Driver's Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning.

Sensors (Basel, Switzerland) Nov 30, 2021
Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drive...
Accidents, Traffic Machine Learning Automobile Driving Deep Learning Eye Movements Humans Cognition
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A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Sensors (Basel, Switzerland) Nov 13, 2021
Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the str...
Automobile Driving Deep Learning Signal Processing, Computer-Assisted Pedestrians Accidents, Traffic Humans Technology
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A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

Sensors (Basel, Switzerland) Nov 8, 2021
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to b...
Neural Networks, Computer Deep Learning Humans Distracted Driving Accidents, Traffic Automobile Driving
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Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition.

Computational intelligence and neuroscience Nov 6, 2021
Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obst...
Deep Learning Machine Learning Accidents, Traffic
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Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach.

International journal of environmental research and public health Nov 3, 2021
In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this pa...
Neural Networks, Computer Accidents, Traffic Machine Learning Algorithms
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Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning.

Traffic injury prevention Oct 26, 2021
OBJECTIVE: The aim of this study is to identify the effects of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents via deep learning.
Finite Element Analysis Craniocerebral Trauma Pedestrians Accidents, Traffic Automobiles Humans Deep Learning Adult Female Walking Child Male
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Accuracy of machine learning logistic regression in death prediction for patients of road traffic injury.

Asian journal of surgery Oct 15, 2021
Data Collection Humans Logistic Models Machine Learning Accidents, Traffic
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