ADAS Classifier for Driver Monitoring and Driving Qualification using Both Internal and External Vehicle Data

Rafael Alceste Berri, Diego Renan Bruno, Eduardo Borges, Giancarlo Lucca, Fernando Santos Osorio

2022

Abstract

In this paper, we present an innovative safety system for driver monitoring and quality of how a vehicle is being controlled by a human driver. The main objective of this work is linked to the goal of detecting human failures in the task of driving, improving the predictions of human failures. In this work, we used 3D information of the driver’s posture and also the vehicles’ behavior on the road. Our proposal is able to act when human inappropriate behaviors are detected by applying a set of automatic routines to minimize their consequences. It is also possible to produce safety alarms/warnings in order to re-educate the driver to maintain good posture practices and to avoid dangerous driving using only few seconds (2.5s) of data capture. This can help to improve traffic, drivers’ education, and benefits with the reduction of accidents. When a highly dangerous behavior/situation is detected, using 140 seconds of recorded data, an autonomous parking system is activated, parking the vehicle in a safe position. We present in this paper new classifiers for ADAS (Advanced Systems of Driver Assistance) based on Machine Learning. Our classifiers are based on Artificial Neural Nets (ANN), furthermore, the values set to adjust input features, neuron activation functions, and network topology/training parameters were optimized and selected using a Genetic Algorithm. The proposed system achieved results of 79.65% of accuracy in different alarm levels (short and long term), for joint detection of risk in situations of cellphone usage, drunkenness, or regular driving. Only 1.8% of normal situations have wrong predictions (false positive alarms) in Naturalistic Driver Behavior Dataset frames, contributing to the driver’s comfort when he/she is using the system. In the near future we aim to improve these results even more.

Paper Citation

in Harvard Style

Berri R., Bruno D., Borges E., Lucca G. and Osorio F. (2022). ADAS Classifier for Driver Monitoring and Driving Qualification using Both Internal and External Vehicle Data. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 560-567. DOI: 10.5220/0010880100003124

in Bibtex Style

@conference{visapp22,
author={Rafael Alceste Berri and Diego Renan Bruno and Eduardo Borges and Giancarlo Lucca and Fernando Santos Osorio},
title={ADAS Classifier for Driver Monitoring and Driving Qualification using Both Internal and External Vehicle Data},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={560-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010880100003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - ADAS Classifier for Driver Monitoring and Driving Qualification using Both Internal and External Vehicle Data
SN - 978-989-758-555-5
AU - Berri R.
AU - Bruno D.
AU - Borges E.
AU - Lucca G.
AU - Osorio F.
PY - 2022
SP - 560
EP - 567
DO - 10.5220/0010880100003124
PB - SciTePress