AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Accidents, Traffic

Showing 221 to 230 of 269 articles

Clear Filters

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

International journal of environmental research and public health
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing r...

Expressway crash risk prediction using back propagation neural network: A brief investigation on safety resilience.

Accident; analysis and prevention
This study presents the work in predicting crash risk on expressways with consideration of both the impact of safety critical events and traffic conditions. The traffic resilience theory is introduced to learn safety problems from the standpoint of 1...

Pedestrian's risk-based negotiation model for self-driving vehicles to get the right of way.

Accident; analysis and prevention
Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are pro...

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements.

Accident; analysis and prevention
Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent adva...

A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data.

Accident; analysis and prevention
According to NHTSA, more than 3477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving be...

A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data.

Accident; analysis and prevention
The primary objective of this study is to investigate how the deep learning approach contributes to citywide short-term crash risk prediction by leveraging multi-source datasets. This study uses data collected from Manhattan in New York City to illus...

The Moral Machine experiment.

Nature
With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To a...

Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network.

Traffic injury prevention
OBJECTIVES: This article focuses on the effect of road lighting on road safety at accesses to quantitatively analyze the relationship between road lighting and road safety.

Effective Vehicle-Based Kangaroo Detection for Collision Warning Systems Using Region-Based Convolutional Networks.

Sensors (Basel, Switzerland)
Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions that occurred only during the year 2015 in Australia. ...

Market penetration of intersection AEB: Characterizing avoided and residual straight crossing path accidents.

Accident; analysis and prevention
Car occupants account for one third of all junction fatalities in the European Union. Driver warning can reduce intersection accidents by up to 50 percent; adding Autonomous Emergency Braking (AEB) delivers a reduction of up to 70 percent. However, t...