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

Sensors

Noélia Correia¹  Faroq Al-Tam¹  JoséManuel Lozano Domínguez²  Tomásde J. Mateo Sanguino² 

[1] Center for Electronic, Optoelectronic and Telecommunications, Faculty of Science and Technology, University of Algarve, 8005-139 Faro, Portugal;Department of Electronic Engineering, Computer Systems and Automatics, University of Huelva, Av. de las Artes s/n, 21007 Huelva, Spain;

关键词: smart road safety;    pedestrian crossings accidents;    vehicle detection;    machine learning;    time series forecasting;   

DOI  :  10.3390/s20216019

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown