AIMC Topic: Automobile Driving

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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
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...

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study.

International journal of environmental research and public health
Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pe...

Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data.

Applied ergonomics
The aim of this study is to investigate the usefulness of the anomaly detection method by one-class support vector machine (OCSVM) for the evaluation of mental workload (MWL) during automobile driving. Twelve students (six males and six females) part...

Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators.

Big data
Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of t...