RT Journal Article
T1 Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks
A1 Lozano Domínguez, José Manuel
A1 Al-Tam, Faroq
A1 Mateo Sanguino, Tomás Jesús
A1 Correia, Noélia
AB Improving road safety through artificial intelligence-based systems is now crucial turningsmart cities into a reality. Under this highly relevant and extensive heading, an approach is proposedto improve vehicle detection in smart crosswalks using machine learning models. Contrarily toclassic fuzzy classifiers, machine learning models do not require the readjustment of labels thatdepend on the location of the system and the road conditions. Several machine learning modelswere trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain.These include random forest, time-series forecasting, multi-layer perceptron, support vector machine,and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-artdouble-deep recurrent Q-network, is also designed and compared with the machine learning modelsjust mentioned. Results show that the machine learning models can efficiently replace the classicfuzzy classifier.
PB MDPI
SN 0959-6526
YR 2020
FD 2020-11
LK http://hdl.handle.net/10272/19112
UL http://hdl.handle.net/10272/19112
LA eng
NO Lozano Domínguez, J. M., Al-Tam, F., Mateo Sanguino, T. de J., & Correia, N. (2020). Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks. Sensors, 20(21), 6019. DOI: https://doi.org/10.3390/s20216019
DS Repositorio Institucional de la Universidad de Huelva
RD 29 may 2026