AIMC Topic: Accidents, Traffic
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Could vehicles analyze driving risks using human fuzzy semantic logic? A data-knowledge-driven new perspective.
Accident; analysis and prevention
Aug 1, 2025
Accurate risk identification is crucial for ensuring the safe operation of Host vehicles (HoVs) in environments shared with Neighboring vehicles (NeVs). Traditional risk identification mechanisms typically rely on large amounts of precise numerical d...
Collision risk prediction and takeover requirements assessment based on radar-video integrated sensors data: A system framework based on LLM.
Accident; analysis and prevention
Aug 1, 2025
There are safety risks when drivers take over the control of autonomous driving vehicles, and reducing unnecessary takeovers is essential to improve driving safety. This study seeks to develop an interpretable system framework for collision risk pred...
A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds.
Accident; analysis and prevention
Aug 1, 2025
In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing ...
Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling - artificial neural networks approach.
Accident; analysis and prevention
Aug 1, 2025
Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes...
Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet).
Accident; analysis and prevention
Jul 1, 2025
Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with fast...
A game theoretical model to examine pedestrian behaviour and safety on unsignalised slip lanes using AI-based video analytics.
Accident; analysis and prevention
Jul 1, 2025
Left-turn slip lanes, also known as channelised right-turn lanes in right-hand driving countries, are widely implemented to facilitate left-turning at signalised intersections. However, pedestrian safety on slip lanes is not well known. At unsignalis...
Spatio-temporal crash severity analysis with cost-sensitive multi-graphs attention network.
Accident; analysis and prevention
Jul 1, 2025
Most conventional crash severity models attempt to achieve a low classification error rate, implicitly assuming the same losses for all classification errors. In this paper, we suggest that this setting has limitations in terms of reasonableness, as ...
Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach.
Scientific reports
May 20, 2025
Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging st...
Modeling of injury severity of distracted driving accident using statistical and machine learning models.
PloS one
Jan 1, 2025
Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted driving accidents (DDAs) is one of the concerns among transportation communities. The present study...
Waist rotation angle as indicator of probable human collision-avoidance direction for autonomous mobile robots.
PloS one
Jan 1, 2025
The likelihood of pedestrians encountering autonomous mobile robots (AMRs) in smart cities is steadily increasing. While previous studies have explored human-to-human collision avoidance, the behavior of humans avoiding AMRs in direct, head-on scenar...
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