Authors:
Rafael Alceste Berri
1
;
Diego Renan Bruno
2
;
Eduardo Borges
1
;
Giancarlo Lucca
1
and
Fernando Santos Osorio
3
Affiliations:
1
Center for Computational Science, Federal University of Rio Grande (FURG), Rio Grande, RS, Brazil
;
2
São Paulo State Faculty of Technology, São Paulo, Brazil
;
3
University of São Paulo, São Paulo, Brazil
Keyword(s):
ADAS, Computer Vision, Autonomous Vehicles, Driver Assistance, Machine Learning.
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 v
ehicle 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.
(More)