AIMC Topic: Accidents, Traffic
Clear Filters Showing 11 to 20 of 290 articles
Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories.
PloS one
May 2, 2025
With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiote...
Cyclist safety in the digital age: A review of advanced warning technologies.
Accident; analysis and prevention
Apr 25, 2025
Improving the safety of cyclists, who are considered vulnerable road users, is essential. Implementing a warning system that alerts cyclists to nearby hazards is an effective method to improve their safety. Nevertheless, the literature needs a compre...
Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.
International journal of injury control and safety promotion
Apr 11, 2025
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...
Children on wheels: Identifying crash determinants using cluster correspondence analysis.
Accident; analysis and prevention
Apr 10, 2025
Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes ...
Optimized driver fatigue detection method using multimodal neural networks.
Scientific reports
Apr 10, 2025
Driver fatigue is a significant factor contributing to road accidents, highlighting the need for precise and reliable detection systems. This study introduces a comprehensive approach using multimodal neural networks, leveraging the DROZY dataset, wh...
Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.
Scientific reports
Mar 29, 2025
Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and ...
Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture.
Accident; analysis and prevention
Mar 22, 2025
The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is generating critical testing scenarios to accurately evaluate vehicle performance. To overcome the limita...
Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.
Scientific reports
Mar 14, 2025
Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This results in numerous unfavourable circumstances that negatively impact people's life. The identification o...
Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.
Accident; analysis and prevention
Mar 4, 2025
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...
Joint Driver State Classification Approach: Face Classification Model Development and Facial Feature Analysis Improvement.
Sensors (Basel, Switzerland)
Feb 27, 2025
Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dual-framework approach that integrates a convolutional neural network (CNN) and a facial landmark anal...
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