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Open AccessArticle

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

1
Department of Electronic Engineering, Computer Systems and Automatics, University of Huelva, Av. de las Artes s/n, 21007 Huelva, Spain
2
Center for Electronic, Optoelectronic and Telecommunications, Faculty of Science and Technology, University of Algarve, 8005-139 Faro, Portugal
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6019; https://doi.org/10.3390/s20216019
Received: 7 September 2020 / Revised: 20 October 2020 / Accepted: 21 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)
Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were 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-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier. View Full-Text
Keywords: smart road safety; pedestrian crossings accidents; vehicle detection; machine learning; time series forecasting smart road safety; pedestrian crossings accidents; vehicle detection; machine learning; time series forecasting
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MDPI and ACS Style

Lozano Domínguez, J.M.; Al-Tam, F.; Mateo Sanguino, T.d.J.; Correia, N. Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks. Sensors 2020, 20, 6019. https://doi.org/10.3390/s20216019

AMA Style

Lozano Domínguez JM, Al-Tam F, Mateo Sanguino TdJ, Correia N. Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks. Sensors. 2020; 20(21):6019. https://doi.org/10.3390/s20216019

Chicago/Turabian Style

Lozano Domínguez, José M.; Al-Tam, Faroq; Mateo Sanguino, Tomás d.J.; Correia, Noélia. 2020. "Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks" Sensors 20, no. 21: 6019. https://doi.org/10.3390/s20216019

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