AIMC Topic: Accidents, Traffic

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A deep transfer learning approach for Real-Time traffic conflict prediction with trajectory data.

Accident; analysis and prevention Feb 17, 2025
Recently, real-time traffic conflict prediction has drawn increasing attention due to its significant potential in proactive traffic safety systems. While various statistical and machine learning models have been developed for conflict prediction, tr...
Humans Deep Learning Accidents, Traffic Automobile Driving
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Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network.

Accident; analysis and prevention Feb 8, 2025
The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and con...
China Cities Environment Design Humans Neural Networks, Computer Spatial Analysis Built Environment Population Density Accidents, Traffic Safety
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Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.

Sensors (Basel, Switzerland) Jan 29, 2025
Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as i...
Face Sleep Stages Machine Learning Algorithms Accidents, Traffic Automobile Driving Support Vector Machine Humans Neural Networks, Computer
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An integrative approach to generating explainable safety assessment scenarios for autonomous vehicles based on Vision Transformer and SHAP.

Accident; analysis and prevention Jan 13, 2025
Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insi...
Humans Artificial Intelligence Automobiles Automobile Driving Automation Accidents, Traffic Safety
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Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection for intelligent transportation: An improved method combining autoencoders and dynamic Bayesian networks.

Accident; analysis and prevention Jan 3, 2025
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technolo...
China Autonomous Vehicles Bayes Theorem Accidents, Traffic Autoencoder Time Factors Models, Statistical
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Automatic brake Driver Assistance System based on deep learning and fuzzy logic.

PloS one Dec 31, 2024
Advanced Driver Assistance Systems (ADAS) aim to automate transportation fully. A key part of this automation includes tasks such as traffic light detection and automatic braking. While indoor experiments are prevalent due to computational demands an...
Accidents, Traffic Deep Learning Humans Automobile Driving Automation Fuzzy Logic Algorithms
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Assessing the impact of car-following driving style on traffic conflict risk using asymmetric behavior model and explainable machine learning.

Accident; analysis and prevention Dec 24, 2024
To deepen the understanding of the impact of car-following driving style (CFDS) on traffic conflict risk and address the lack of clear CFDS evaluation metrics, this study proposes an improved CFDS metric based on the Asymmetric Behavior (AB) theory. ...
Aggression Risk-Taking Accidents, Traffic Automobile Driving Machine Learning Humans Risk Assessment Automobiles
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Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections.

Accident; analysis and prevention Dec 19, 2024
Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This rese...
Deep Learning China Automobiles Accidents, Traffic Environment Design Humans Safety Automation Automobile Driving
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Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection.

Sensors (Basel, Switzerland) Dec 12, 2024
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and ...
Automobile Driving Video Recording Semantics Algorithms Accidents, Traffic Fatigue Humans Neural Networks, Computer
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Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State.

Sensors (Basel, Switzerland) Dec 6, 2024
Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on con...
Fatigue Yawning Machine Learning Algorithms Accidents, Traffic Humans Image Processing, Computer-Assisted Automobile Driving Neural Networks, Computer
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