AIMC Topic: Accidents, Traffic

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The Modeling of Super Deep Learning Aiming at Knowledge Acquisition in Automatic Driving.

Computational intelligence and neuroscience Apr 9, 2022
In this paper, we proposed a new theory of solving the multitarget control problem by introducing a machine learning framework in automatic driving and implementing the acquisition of excellent drivers' knowledge. Nowadays, there still exist some cor...
Automobile Driving Deep Learning Accidents, Traffic Machine Learning
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E-scooter related injuries: Using natural language processing to rapidly search 36 million medical notes.

PloS one Apr 6, 2022
BACKGROUND: Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip.
Humans Retrospective Studies Natural Language Processing Motorcycles Emergency Service, Hospital Accidents, Traffic
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Farm Vehicle Following Distance Estimation Using Deep Learning and Monocular Camera Images.

Sensors (Basel, Switzerland) Apr 2, 2022
This paper presents a comprehensive solution for distance estimation of the following vehicle solely based on visual data from a low-resolution monocular camera. To this end, a pair of vehicles were instrumented with real-time kinematic (RTK) GPS, an...
Farms Automobile Driving Motor Vehicles Deep Learning Accidents, Traffic
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Transferability of multivariate extreme value models for safety assessment by applying artificial intelligence-based video analytics.

Accident; analysis and prevention Mar 31, 2022
Traffic conflict techniques represent the state-of-the-art for road safety assessments. However, the lack of research on transferability of conflict-based crash risk models, which refers to applying the developed crash risk estimation models to a set...
Safety Calibration Models, Statistical Accidents, Traffic Environment Design Australia Humans Artificial Intelligence
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Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots.

Scientific reports Mar 28, 2022
Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults ...
Robotics Pedestrians Accidental Falls Child Accidents, Traffic Craniocerebral Trauma Humans Aged
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Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention Mar 24, 2022
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...
Humans Unsupervised Machine Learning Heart Rate Automobile Driving Bayes Theorem Accidents, Traffic
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A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach.

International journal of environmental research and public health Mar 6, 2022
Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver as...
Automobile Driving Emotions Accidents, Traffic Humans Neural Networks, Computer Artificial Intelligence Machine Learning
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E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model.

Sensors (Basel, Switzerland) Feb 26, 2022
The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accide...
Deep Learning Accidents, Traffic Attention Automobile Driving Distracted Driving
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On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development.

Accident; analysis and prevention Feb 21, 2022
Machine learning (ML) model interpretability has attracted much attention recently given the promising performance of ML methods in crash frequency studies. Extracting accurate relationship between risk factors and crash frequency is important for un...
Risk Factors Humans Accidents, Traffic Machine Learning
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Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents.

PloS one Feb 2, 2022
To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machi...
Wounds and Injuries Humans Accidents, Traffic ROC Curve Pakistan Machine Learning Area Under Curve Logistic Models Severity of Illness Index Bayes Theorem
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