RT Journal Article
T1 IVVI 2.0: An intelligent vehicle based on computational perception
A1 Martín Gómez, David
A1 García Fernández, Fernando
A1 Musleh Lancis, Basam
A1 Olmeda Reino, Daniel
A1 Peláez Coronado, Gustavo Adolfo
A1 Marín Plaza, Pablo
A1 Ponz Vila, Aurelio
A1 Rodríguez Urbano, Francisco José
A1 Al-Kaff, Abdulla Hussein
A1 Escalera Hueso, Arturo de la
A1 Armingol Moreno, José María
AB This paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Computational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting-edge applications to cope with perception difficulties, data processing algorithms, expert knowledge, and decision-making. The long-term in-vehicle applications, that are presented in this paper, outperform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and others vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning systems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.
SN 0957-4174
YR 2014
FD 2014-12-01
LK https://hdl.handle.net/10016/23608
UL https://hdl.handle.net/10016/23608
LA eng
NO This work was supported by the Spanish Government through the CICYT projects (GRANT TRA2010 20225 C03 01) and (GRANT TRA 2011 29454 C03 02).
DS e-Archivo
RD 1 may. 2024