English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/165061
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

AuthorsCastaño, Fernando ; Beruvides, Gerardo ; Villalonga, Alberto; Haber Guerra, Rodolfo E.
Issue Date10-May-2018
PublisherMultidisciplinary Digital Publishing Institute
CitationSensors 18 (5): 1508 (2018)
AbstractOn-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the ‘Internet of Things’ (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds.
Publisher version (URL)http://dx.doi.org/10.3390/s18051508
URIhttp://hdl.handle.net/10261/165061
DOIhttp://dx.doi.org/doi: 10.3390/s18051508
Identifiersdoi: 10.3390/s18051508
Appears in Collections:(CAR) Artículos
Files in This Item:
File Description SizeFormat 
sensors-18-01508.pdf36,48 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.