AIMC Topic: Accidents, Traffic
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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...
An intelligent network framework for driver distraction monitoring based on RES-SE-CNN.
Scientific reports
Feb 26, 2025
As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample resear...
Simulation of human-vehicle interaction at right-turn unsignalized intersections: A game-theoretic deep maximum entropy inverse reinforcement learning method.
Accident; analysis and prevention
Feb 19, 2025
The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportation framework, pedestrians encounter heightened safety risks in complex urban road environments. Prote...
Comparing AI and human-generated health messages in an Arabic cultural context.
Global health action
Feb 18, 2025
BACKGROUND: AI is rapidly transforming the design of communication messages across various sectors, including health and safety. However, little is known about its effectiveness for roughly 420 million native Arabic speakers worldwide.
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