ArticleSensors (Basel, Switzerland)2018
Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Advances in LiDAR Hardware Technology: Focus on Elastic LiDAR for Solid Target Scanning.Sensors (Basel, Switzerland) · 2024Review
- A Study of Common Principles for Decision-Making in Moral Dilemmas for Autonomous Vehicles.Behavioral sciences (Basel, Switzerland) · 2022Article
- Analysis and Prospect of the Application of Wireless Sensor Networks in Ubiquitous Power Internet of Things.Computational intelligence and neuroscience · 2022Review
- Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks.ACS omega · 2021Article
- The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots.Computational intelligence and neuroscience · 2021Article
- An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system.PeerJ. Computer science · 2021Article
- Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2020Article
- Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle.Sensors (Basel, Switzerland) · 2020Article
- A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images.Computational intelligence and neuroscience · 2020Article
- Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain.Sensors (Basel, Switzerland) · 2019Article
- Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports.Sensors (Basel, Switzerland) · 2019Article
- A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints.Sensors (Basel, Switzerland) · 2019Article
- Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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