AIMC Topic: Accidents, Traffic
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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...
Artificial Intelligence-Driven All-Terrain Vehicle Crash Prediction and Prevention System.
Journal of agricultural safety and health
Oct 21, 2024
HIGHLIGHTS: An AI-driven system for predicting and preventing ATV crashes was developed. Machine learning model achieved rollover prediction accuracy of over 99%. The system has the potential to significantly reduce ATV-related injuries and fatalitie...
The Future of Road Safety: Challenges and Opportunities.
The Milbank quarterly
Apr 1, 2023
Policy Points Traditional approaches to addressing motor vehicle crashes are yielding diminishing returns. A comprehensive strategy known as the Safe Systems approach shows promise in both advancing safety and equity and reducing motor vehicle crashe...
Machine learning to predict passenger mortality and hospital length of stay following motor vehicle collision.
Neurosurgical focus
Apr 1, 2022
OBJECTIVE: Motor vehicle collisions (MVCs) account for 1.35 million deaths and cost $518 billion US dollars each year worldwide, disproportionately affecting young patients and low-income nations. The ability to successfully anticipate clinical outco...
Road traffic accident trauma: A model for road safety management utilizing the artificial intelligence with geo-mapping and geospatial data in Pakistan.
JPMA. The Journal of the Pakistan Medical Association
Mar 1, 2022
Application of Deep Learning Methods for Pedestrian Collision Detection Using Dashcam Videos.
Stapp car crash journal
Nov 1, 2020
The goal of this study is to clarify the usefulness of deep learning methods for pedestrian collision detection using dashcam videos for advanced automatic collision notification, focusing on pedestrians, as they make up the highest number of traffic...
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