AIMC Topic: Accidents, Traffic

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Predicting lane change maneuver and associated collision risks based on multi-task learning.

Accident; analysis and prevention Nov 4, 2024
The lane-changing (LC) maneuver of vehicles significantly impacts highway traffic safety. Therefore, proactively predicting LC maneuver and associated collision risk is of paramount importance. However, most of the previous LC risk prediction researc...
Risk Assessment Accidents, Traffic Automobile Driving Humans Neural Networks, Computer
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Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds.

Sensors (Basel, Switzerland) Oct 24, 2024
People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study ai...
Sound Awareness Algorithms Neural Networks, Computer Humans Accidents, Traffic
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Predicting pedestrian-vehicle interaction severity at unsignalized intersections.

Traffic injury prevention Oct 15, 2024
OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptron...
Safety Humans Deep Learning Pedestrians Accidents, Traffic Environment Design
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Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning.

Accident; analysis and prevention Oct 7, 2024
Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effecti...
Cities Environment Design China Time Factors Causality Accidents, Traffic Humans Machine Learning
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An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.

Traffic injury prevention Oct 2, 2024
OBJECTIVE: In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety.
Machine Learning Accidents, Traffic Humans Algorithms Attention Risk Assessment Automobile Driving Models, Theoretical
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Uncertainty-aware probabilistic graph neural networks for road-level traffic crash prediction.

Accident; analysis and prevention Oct 2, 2024
Traffic crashes present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic crash prediction model is crucial to address growing public safety concerns and improve the sa...
Humans Neural Networks, Computer Risk Assessment Accidents, Traffic Deep Learning Uncertainty Models, Statistical
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A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.

Sensors (Basel, Switzerland) Sep 27, 2024
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this...
Automobile Driving Sleepiness Accidents, Traffic Mouth Neural Networks, Computer Humans Eye Artificial Intelligence Sleep Stages
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Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.

Traffic injury prevention Sep 26, 2024
OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe...
Safety Video Recording Wounds and Injuries Regression Analysis Accidents, Traffic Environment Design Models, Statistical Humans Neural Networks, Computer Machine Learning
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Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework.

Traffic injury prevention Sep 26, 2024
OBJECTIVE: This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.
Humans Machine Learning Railroads Accidents, Traffic Environment Design Safety Cluster Analysis
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Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method.

PloS one Sep 24, 2024
Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned abo...
Bayes Theorem Accidents, Traffic Cities Humans Support Vector Machine China
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