AI Medical Compendium Topic

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

Automobile Driving

Showing 151 to 160 of 234 articles

Clear Filters

Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.

Accident; analysis and prevention
Transportation safety is highly correlated with driving behavior, especially human error playing a key role in a large portion of crashes. Modern instrumentation and computational resources allow for the monitorization of driver, vehicle, and roadway...

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data.

International journal of environmental research and public health
BACKGROUND: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the well...

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.

Sensors (Basel, Switzerland)
Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fa...

Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data.

Accident; analysis and prevention
Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. ...

PORF-DDPG: Learning Personalized Autonomous Driving Behavior with Progressively Optimized Reward Function.

Sensors (Basel, Switzerland)
Autonomous driving with artificial intelligence technology has been viewed as promising for autonomous vehicles hitting the road in the near future. In recent years, considerable progress has been made with Deep Reinforcement Learnings (DRLs) for rea...

Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles.

Accident; analysis and prevention
The era of Big Data has arrived. Recently, under the environment of intelligent transportation systems (ITS) and connected/automated vehicles (CAV), Big Data has been applied in various fields in transportation including traffic safety. In this study...

Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety.

Sensors (Basel, Switzerland)
To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the...

An integrated architecture for intelligence evaluation of automated vehicles.

Accident; analysis and prevention
Increasing automation calls for evaluating the effectiveness and intelligence of automated vehicles. This paper proposes a framework for quantitatively evaluating the intelligence of automated vehicles. Firstly, we establish the evaluation environmen...

Machine-learning approach to predict on-road driving ability in healthy older people.

Psychiatry and clinical neurosciences
AIM: In Japan, fatal traffic accidents due to older drivers are on the rise. Considering that approximately half the older drivers who have caused fatal accidents are cognitively normal healthy people, it has been required to detect older drivers who...