Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue,
distraction, drunkenness, and/or recklessness are the most common types of abnormal …

A growing number of sensors, embedded in wearables, smart electric meters and other
connected devices, is surrounding us and reaching ever deeper into our private lives. While …

This paper presents a methodology and mobile application for driver monitoring, analysis,
and recommendations based on detected unsafe driving behavior for accident prevention …

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things
(IoT) and cyber–physical systems (CPS). One characteristic of CPS is the reciprocal …

Drivers' behavior evaluation is one of the most important problems in intelligent
transportation systems and driver assistant systems. It has a great influence on driving safety …

Aggressive driving behavior is one of the leading causes of road accidents worldwide. One
way to ameliorate this situation is to collect and analyze driving patterns with the intention of …

Driving behavior classification is highly correlated with vehicle accidents and injury.
Automatically recognizing different driving behaviors is important for improving road safety …

Driving behavior recognition is a challenging task that exploits the acceleration and angular
velocity information of the vehicle collected by smartphone to identify various driving events …

Usage-based insurance schemes provide new opportunities for insurers to accurately price
and manage risk. These schemes have the potential to better identify risky drivers which not …

Driver distraction is one of the major causes of traffic accidents. In recent years, given the
advance in connectivity and social networks, the use of smartphones while driving has …