Research of Automotive Controller Area Network Bus Detection System

Cui, Hong Wei

doi:10.4028/www.scientific.net/amr.662.736

Cited by 1 publication

(1 citation statement)

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“…The controller area network (CAN) was adopted by the automotive industry in 1993 and since then it is integrated in every vehicle manufactured so as to gather data from the vehicle’s sensors [ 26 ]. In 1993 CAN became an international standard known as ISO11898, later revised in 2015 [ 27 ].…”

Section: Data Collection and Analysis Infrastructurementioning

confidence: 99%

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data

Πεππές

Alexakis

Adamopoulou

et al. 2021

Sensors

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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 CO2 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 algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.

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Section: Data Collection and Analysis Infrastructurementioning

confidence: 99%