AIMC Topic: Accidents, Traffic

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A machine learning approach to understanding the road and traffic environments of crashes involving driver distraction and inattention (DDI) on rural multilane highways.

Journal of safety research Nov 14, 2024
INTRODUCTION: Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving...
Machine Learning Attention Distracted Driving Rural Population Automobile Driving Humans Accidents, Traffic
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Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Accident; analysis and prevention Nov 12, 2024
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account...
Automobile Driving Decision Trees Electroencephalography Accidents, Traffic Stress, Psychological Fuzzy Logic Brain Female Adult Male Young Adult Humans Machine Learning Algorithms
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Research on recognition of slippery road surface and collision warning system based on deep learning.

PloS one Nov 11, 2024
Aiming at the problems of slow detection speed, large prediction error and weak environmental adaptability of current vehicle collision warning system, this paper proposes a recognition method of slippery road surface and collision warning system bas...
Deep Learning Accidents, Traffic Automobile Driving Humans Algorithms
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Detecting Emotional Arousal and Aggressive Driving Using Neural Networks: A Pilot Study Involving Young Drivers in Duluth.

Sensors (Basel, Switzerland) Nov 5, 2024
Driving is integral to many people's daily existence, but aggressive driving behavior increases the risk of road traffic collisions. Young drivers are more prone to aggressive driving and danger perception impairments. A driver's physiological state ...
Adolescent Emotions Automobile Driving Arousal Accidents, Traffic Humans Adult Galvanic Skin Response Female Neural Networks, Computer Young Adult Male Aggression Pilot Projects
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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...
Automobile Driving Humans Risk Assessment Accidents, Traffic 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...
Awareness Humans Accidents, Traffic Neural Networks, Computer Algorithms Sound
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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...
Accidents, Traffic Safety Humans Environment Design Pedestrians Deep Learning
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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...
Machine Learning China Time Factors Humans Causality Accidents, Traffic Cities Environment Design
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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.
Humans Machine Learning Algorithms Accidents, Traffic Attention Automobile Driving Risk Assessment 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 Uncertainty Deep Learning Models, Statistical Accidents, Traffic Neural Networks, Computer Risk Assessment
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