AIMC Topic: Automobile Driving

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Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state.

Physiological measurement Jan 1, 2021
OBJECTIVE: Our objective is to study how to obtain features which can reflect the continuity and internal dynamic changes of electroencephalography (EEG) signals and study an effective method for fatigued driving state recognition based on the obtain...
Data Analysis Algorithms Humans Automobile Driving Fatigue Support Vector Machine Electroencephalography
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Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.

Accident; analysis and prevention Dec 29, 2020
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 Accidents, Traffic Humans Automobile Driving Distracted Driving
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Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data.

International journal of environmental research and public health Dec 18, 2020
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...
Automobile Driving Text Messaging Deep Learning Accidents, Traffic Humans
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InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.

Sensors (Basel, Switzerland) Dec 17, 2020
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...
Electroencephalography Automobile Driving Machine Learning Support Vector Machine Fatigue Humans Neural Networks, Computer
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Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data.

Accident; analysis and prevention Dec 8, 2020
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. ...
Neural Networks, Computer Machine Learning Humans Deep Learning Automobile Driving Accidents, Traffic
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The future of AI in critical care is augmented, not artificial, intelligence.

Critical care (London, England) Dec 2, 2020
Critical Care Artificial Intelligence Algorithms Humans Forecasting Automobile Driving
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PORF-DDPG: Learning Personalized Autonomous Driving Behavior with Progressively Optimized Reward Function.

Sensors (Basel, Switzerland) Oct 1, 2020
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...
Humans Neural Networks, Computer Automobile Driving Artificial Intelligence Reward
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Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles.

Accident; analysis and prevention Sep 4, 2020
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...
Automation Transportation Research Design Big Data Safety Humans Accidents, Traffic Automobile Driving Artificial Intelligence
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Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety.

Sensors (Basel, Switzerland) Aug 19, 2020
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...
Automation Artificial Intelligence Transportation Machine Learning Automobile Driving
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An integrated architecture for intelligence evaluation of automated vehicles.

Accident; analysis and prevention Jul 24, 2020
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...
Accidents, Traffic Automobile Driving Automation Artificial Intelligence Algorithms Humans
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