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Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data by Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou; Konstantinos Demestichas is a Engineering article available to read on EtoBox.

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In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO~2~ emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning a

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Author

Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou; Konstantinos Demestichas

Publisher

MDPI AG

Published

2021

Language

EN

Field

Engineering (Physical Sciences)