This study reviews the Artificial Intelligence and Machine Learning approaches developed
thus far for driver profile and driving pattern recognition, representing a set of macroscopic …

Driving pattern recognition has been applied for the purposes of driving styles identification
and harsh driving events detection. However, the evolution of driving behavior around and …

The human factor is one of the most critical parameters in car accidents and even traffic
occurrences. Driving style affected by human factors comprises driving events (maneuvers) …

Drivers' improper driving behavior plays a vital role in road accidents. Different approaches
have been proposed to classify and evaluate driving performance to ensure road safety …

Insurance Telematics is a recent service that is offered to drivers where a sensing platform
measures vehicle's dynamics with the ultimate goal of inferring the level of risk that the driver …

Driving behavior primitives play a crucial role in semantic explanation of driving behaviors.
Although much work has been done on exacting driving behavior primitives from naturalistic …

Multivariate Time Series (MTS) data capture temporal behaviors to provide invaluable
insights into various physical dynamic phenomena. In smart mobility, MTS plays a crucial …

In the automotive industry, sensors collect data that contain valuable driving information. The
collected datasets are in multivariate time series (MTS) format, which are noisy, non …

Rising car ownership rates lead to increased congestion, especially in inner-city areas,
which consequently results in a deterioration of the quality of life for residents. Although it is …

The identification of maneuver law of enemy maneuvering targets is of great significance for
determining the threat level of the target. We aim to improve the generalization performance …