Motorcycle detection for ADAS through camera and V2V communication, a comparative analysis of two modern technologies

Anaya Catalán, José Javier; Talavera Muñoz, Edgar; Ponz, Aurelio y García, Fernando (2017). Motorcycle detection for ADAS through camera and V2V communication, a comparative analysis of two modern technologies. "Expert Systems with Applications", v. 77 ; pp. 148-159. ISSN 0957-4174. https://doi.org/http://dx.doi.org/10.1016/j.eswa.2017.01.032.

Descripción

Título: Motorcycle detection for ADAS through camera and V2V communication, a comparative analysis of two modern technologies
Autor/es:
  • Anaya Catalán, José Javier
  • Talavera Muñoz, Edgar
  • Ponz, Aurelio
  • García, Fernando
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems with Applications
Fecha: Julio 2017
Volumen: 77
Materias:
Escuela: E.T.S.I. de Sistemas Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Motorcycles are one of the most dangerous means of transportation. Its death toll is higher than in others, due to the inherent vulnerability of motorcycle drivers. The latest strategies in Advanced Driving Assistance Systems (ADAS) are trying to mitigate this problem by applying the advances of modern technologies to the road transport. This paper presents two different approaches on motorcycle protection, based on two of the most modern available technologies in ADAS, i.e. Computer Vision and Vehicle to Vehicle Communication (V2V). The first approach is based on data fusion of Laser Scanner and Computer Vision, providing accurate obstacle detection and localization based on laser scanner, and obstacle classification using computer vision and laser. The second approach is based on ad-hoc V2V technology and provides detection in case of occlusion for visual sensors. Both technologies have been tested in the presented work, and a performance comparison is given. Tests performed in different driving situations allows to measure the performance of every algorithm and the limitations of each of them based on empirical and scientific foundations. The conclusions of the presented work help foster of expert systems in the automotive sector by providing further discussion of the viability and impact from each of these systems in real scenarios.

Más información

ID de Registro: 44792
Identificador DC: http://oa.upm.es/44792/
Identificador OAI: oai:oa.upm.es:44792
Identificador DOI: http://dx.doi.org/10.1016/j.eswa.2017.01.032
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0957417417300416
Depositado por: Memoria Investigacion
Depositado el: 31 Mar 2017 16:43
Ultima Modificación: 31 Mar 2017 16:43
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