F
Fernando García
Researcher at Charles III University of Madrid
Publications - 114
Citations - 2320
Fernando García is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Sensor fusion & Object detection. The author has an hindex of 22, co-authored 108 publications receiving 1526 citations. Previous affiliations of Fernando García include Complutense University of Madrid & Carlos III Health Institute.
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Proceedings ArticleDOI
BirdNet: A 3D Object Detection Framework from LiDAR Information
Jorge Beltran,Carlos Guindel,Francisco Miguel Moreno,Daniel Cruzado,Fernando García,Arturo de la Escalera +5 more
TL;DR: LiDAR-based 3D object detection pipeline is presented in this article, where LiDAR information is projected into a novel cell encoding for bird's eye view projection and both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing.
Journal ArticleDOI
A Review of Sensor Technologies for Perception in Automated Driving
TL;DR: A snapshot of the future challenges for sensing technologies and perception is presented, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles.
Journal ArticleDOI
Survey of computer vision algorithms and applications for unmanned aerial vehicles
TL;DR: The most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera.
Journal ArticleDOI
Sensor Fusion Methodology for Vehicle Detection
TL;DR: The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriageways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles.
Proceedings ArticleDOI
P2V and V2P communication for Pedestrian warning on the basis of Autonomous Vehicles
TL;DR: A collision prediction algorithm is proposed based on Pedestrian to Vehicle (P2V) and Vehicle topedestrian (V2P) communication technologies, which increases the visual situational awareness of VRU regarding the nearby location of both autonomous and manually-controlled vehicles in a user-friendly form.