Self-tuning method for increased obstacle detection reliability based on internet of things LiDAR sensor models

Castaño Romero, Fernando and Beruvides López, Gerardo and Villalonga Jaén, Alberto and Haber Guerra, Rodolfo E. (2018). Self-tuning method for increased obstacle detection reliability based on internet of things LiDAR sensor models. "Sensors", v. 18 (n. 5); pp. 1-16. ISSN 1424-8220. https://doi.org/10.3390/s18051508.

Description

Title: Self-tuning method for increased obstacle detection reliability based on internet of things LiDAR sensor models
Author/s:
  • Castaño Romero, Fernando
  • Beruvides López, Gerardo
  • Villalonga Jaén, Alberto
  • Haber Guerra, Rodolfo E.
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 2018
ISSN: 1424-8220
Volume: 18
Subjects:
Freetext Keywords: LiDAR sensors reliability; Internet of Things; self-turning parameterization; k-nearest neighbors; driven-assistance simulator
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

On-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. TheWebots 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.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020692480IoSenseUnspecifiedFlexible FE/BE Sensor Pilot Line for the Internet of Everything

More information

Item ID: 56357
DC Identifier: https://oa.upm.es/56357/
OAI Identifier: oai:oa.upm.es:56357
DOI: 10.3390/s18051508
Official URL: https://www.mdpi.com/1424-8220/18/5/1508
Deposited by: Memoria Investigacion
Deposited on: 26 Aug 2022 06:18
Last Modified: 26 Aug 2022 06:18
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