MACHINE LEARNING APPLIED FOR AID TO DRIVER USING RASPBERRY PI
Keywords:
Driver’s Aid; Machine Learning; Computer Vision; Artificial Intelligence.Abstract
Brazil has the fifth highest death toll in the planet. Generally accidents are caused by human failure, involving inattention and disrespect to the law. In order to help the driver to act in a preventive and responsible manner, computer systems can establish ways to issue alerts when recognizing situations of risk to the safety in the traffic. The challenge of this work was to perform the detection and recognition of some traffic signals considered necessary for road safety. This work aimed at the development of an embedded system of assistance to the driver based on computer vision and machine learning. The function of the system is to recognize dangerous situations and alert the driver to the signals found on the tracks (maximum permissible speed, stop, preference and bearing tracks). We used a Raspberry Pi 3 and a camera of 5 megapixels to be the embedded hardware. The work aimed the development of algorithms that perform the task of assisting human perception in guiding vehicles, with execution in low-processing hardware in real time.
Downloads
References
ANDRADE, D. C. Estratégia para detecção e rastreamento de faixas rodoviárias utilizando uma câmera monocular. 2017. Dissertação (Mestrado em Engenharia Elétrica) – Universidade Tecnológica Federal do Paraná. Ponta Grossa, 2017.
HOELSCHER, I. G. Detecção e classificação de sinalização vertical de trânsito em cenários complexos. 2017. Dissertação (Mestrado em Engenharia Elétrica) – Universidade Federal do Rio Grande do Sul. Porto Alegre, 2017.
CNT – CONFEDERAÇÃO NACIONAL DE TRANSPORTE. Pesquisa rodoviária 2018. Disponível em: http://www.cnt.org.br. Acesso em: 17 dez. 2018.
ONSV. 90% dos acidentes são causados por falhas humanas, alerta ONSV Disponível em:
http://www.onsv.org.br/noticias/90-dos-acidentes-sao-causados-por-falhas-humanas-alerta-observatorio/. Acesso em: 17 dez. 2017.
DANESCU, R.; NEDEVSCHI, S. Detection and classification of painted road objects for intersection assistance applications. In: INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC 2010), 13., Proceedings […] Funchal, Portugal, 2010, p. 433–438. https://doi.org/10.1109/ITSC.2010.5625261
GONZALEZ, R. C.; WOODS, R. E. Digital Image Processing. 3. ed. São Paulo: Pearson Prentice Hall, 2010. Disponível em: https://bv4.digitalpages.com.br/ Acesso em: 17 dez. 2018.
LE, T. T.; TRAN, S. T.; MITA, S.; NGUYEN, T. D. Real Time Traffic Sign Detection Using Color and Shape-Based Features. In: ACIIDS'10 PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION AND DATABASE SYSTEMS: Part II. Proceeding [...] Springer-Verlag Berlin, Heidelberg, 2010, p 3-9. https://doi.org/10.1007/978-3-642-12101-2_28
LORSAKUL, A.; SUTHAKORN, J. Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV. In: INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI 2007), 4., Proceedings… POSTECH, PIRO, KOREA, Nov 22-24, 2007. p. 279-284.
NAN, Z.; WEI, P.; XU, L.; ZHENG, N. Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering. Sensors 2016, v. 16, n.8, p. 1276, 2016.https://doi.org/10.3390/s16081276
SUCHITRA, S.; SATZODA, R.; SRIKANTHAN, T. Identifying lane types: A modular approach. In: INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC 2013), 16., Proceedings […] The Hague, Netherlands, 2013, p. 1929–1934. https://doi.org/10.1109/ITSC.2013.6728511
VISVIKIS, C.; SMITH, T. L.; PITCHER, M.; SMITH, R, Study on lane departure warning and lane change assistant systems. (PPR 374). Wokingham, UK: Transport Research Laboratory, 2008.