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

1
Centre for Automation and Robotics, UPM—CSIC, 28500 Arganda del Rey, Spain
2
Research Centre of Advanced and Sustainable Manufacturing, UM, Matanzas 44100, Cuba
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1508; https://doi.org/10.3390/s18051508
Received: 28 February 2018 / Revised: 1 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
(This article belongs to the Special Issue I3S 2017 Selected Papers)
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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. 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. View Full-Text
Keywords: LiDAR sensors reliability; Internet of Things; self-turning parameterization; k-nearest neighbors; driven-assistance simulator LiDAR sensors reliability; Internet of Things; self-turning parameterization; k-nearest neighbors; driven-assistance simulator
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Castaño, F.; Beruvides, G.; Villalonga, A.; Haber, R.E. Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models. Sensors 2018, 18, 1508.

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