IVVI 2.0: An intelligent vehicle based on computational perception Articles uri icon

publication date

  • December 2014

start page

  • 7927

end page

  • 7944

issue

  • 17

volume

  • 41

International Standard Serial Number (ISSN)

  • 0957-4174

Electronic International Standard Serial Number (EISSN)

  • 1873-6793

abstract

  • 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. (C) 2014 Elsevier Ltd. All rights reserved.

subjects

  • Robotics and Industrial Informatics

keywords

  • intelligent transportation systems; computer vision; ir detection; data fusion; vehicle positioning; freeway incident detection; function neural-network; pedestrian detection; urban environments; computer vision; fusion; algorithm; classification; localization; navigation