Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/5/1508 |
| _version_ | 1811213353958244352 |
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| author | Fernando Castaño Gerardo Beruvides Alberto Villalonga Rodolfo E. Haber |
| author_facet | Fernando Castaño Gerardo Beruvides Alberto Villalonga Rodolfo E. Haber |
| author_sort | Fernando Castaño |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T05:45:25Z |
| format | Article |
| id | doaj.art-70f777fca3664c5088889b6b4afda33b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T05:45:25Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-70f777fca3664c5088889b6b4afda33b2022-12-22T03:45:29ZengMDPI AGSensors1424-82202018-05-01185150810.3390/s18051508s18051508Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor ModelsFernando Castaño0Gerardo Beruvides1Alberto Villalonga2Rodolfo E. Haber3Centre for Automation and Robotics, UPM—CSIC, 28500 Arganda del Rey, SpainCentre for Automation and Robotics, UPM—CSIC, 28500 Arganda del Rey, SpainCentre for Automation and Robotics, UPM—CSIC, 28500 Arganda del Rey, SpainCentre for Automation and Robotics, UPM—CSIC, 28500 Arganda del Rey, SpainOn-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.http://www.mdpi.com/1424-8220/18/5/1508LiDAR sensors reliabilityInternet of Thingsself-turning parameterizationk-nearest neighborsdriven-assistance simulator |
| spellingShingle | Fernando Castaño Gerardo Beruvides Alberto Villalonga Rodolfo E. Haber Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models Sensors LiDAR sensors reliability Internet of Things self-turning parameterization k-nearest neighbors driven-assistance simulator |
| title | Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models |
| title_full | Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models |
| title_fullStr | Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models |
| title_full_unstemmed | Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models |
| title_short | Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models |
| title_sort | self tuning method for increased obstacle detection reliability based on internet of things lidar sensor models |
| topic | LiDAR sensors reliability Internet of Things self-turning parameterization k-nearest neighbors driven-assistance simulator |
| url | http://www.mdpi.com/1424-8220/18/5/1508 |
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