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

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{∞}control strategy to automated driving systems will be explored in the future [17,18].

## 2. Self-Tuning Method for Reliability in LiDAR Sensors Network

#### 2.1. Conceptual Design

#### 2.2. Implementation

#### 2.2.1. Supervisor Node Controller

#### 2.2.2. Computational Intelligence Library for Modeling

- Calculate the distance between test data and each row of training data
- Sort the calculated distances in ascending order based on distance values
- Select the top k rows from the sorted array
- Select the most frequent class of these rows
- Return the predicted class

#### 2.2.3. Threshold Detector and Q-Learning Procedure

_{t}

_{+1}is the reward observed after performing a

_{t}in ${s}_{t}$, and α is the learning rate (0 < α ≤ 1).

## 3. IoT LiDAR Sensor Models for Obstacle Detection—A Case Study

#### 3.1. Training Dataset from 3D Scenario Simulation

#### 3.1.1. Benchmark Data

_{0}= 0, Y

_{0}= 0 and Z

_{0}= 0), and the fourth column listed the number of the corresponding layer.

_{0}, Y

_{0}, Z

_{0}) point cloud that corresponded to each obstacle.

#### 3.1.2. Model Inputs

^{t}, y

^{t}, z

^{t}} is a location of the median center of an iterative candidate. An important property of a point pattern is the degree of its spatial spread. It can be characterized by the standard distance, SD, that is estimated as

_{i}, y

_{i}, and z

_{i}are the coordinates of point i{x

_{i}, y

_{i}, z

_{i}}, N is the total number of points, and MC(X), MC(Y), and MC(Z) are the coordinates of the mean center. The standard distance that results from the different average distances to a given centroid is usually graphically represented in a geographic information system (GIS) environment by a standard deviational circle, centered on the mean center with the radius equal to the standard distance.

_{i}, y

_{i}, and z

_{i}are the coordinates of point i{x

_{i}, y

_{i}, z

_{i}}, n is the total number of points and the MC is the mean center by geographic coordinates {X, Y, Z}, as calculated in Equation (3).

#### 3.1.3. Model Output

_{ti}, y

_{ti}, z

_{ti}) are the coordinates of the tag at time t

_{i}, and (x

_{Actual,ti}, y

_{Actual,ti}, z

_{Actual,ti}) are the actual coordinates of the tag at time t

_{i}.

#### 3.2. Model Training and Initial Validation

^{4}epochs were trained. The learning rate (μ) initial value was 10

^{−3}with a decrease factor ratio of 10

^{−1}, an increase factor ratio of 10, and a maximum μ value of 10

^{10}. The minimum performance gradient was 10

^{−7}. The adaptive value μ is increased by 10 until the change above results in a reduced performance value. The change is then made to the network and μ is decreased by 10

^{−1}. Training stops when any of these conditions occurs as follows: the maximum number of epochs (repetitions) is reached, or the maximum amount of time is exceeded, or the performance is minimized to the goal, or the performance gradient falls below minimum gradient, or μ exceeds 10

^{10}.

^{2}) were estimated for all the models implemented in the modeling library.

## 4. Experimental Results

^{2}) of each type of model (ANN, kNN, and regression models) according to the number of LiDARs (1, 3, or 5) used at each instant with the objective of expanding the field of view, both vertical and horizontal, of the IoT LiDAR network in different critical situations. It must be stressed that the IoT sensors could not exchange sensory information without a local pre-processing phase in each IoT node. In this case, the shared information is the predicted error values obtained from each cloud point given by each sensor rather than raw sensory information.

## 5. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

- Pajares Redondo, J.; Prieto González, L.; García Guzman, J.; Boada, B.L.; Díaz, V. Vehiot: Design and evaluation of an iot architecture based on low-cost devices to be embedded in production vehicles. Sensors
**2018**, 18, 486. [Google Scholar] [CrossRef] [PubMed] - Krasniqi, X.; Hajrizi, E. Use of iot technology to drive the automotive industry from connected to full autonomous vehicles. IFAC-PapersOnLine
**2016**, 49, 269–274. [Google Scholar] [CrossRef] - Vivacqua, R.; Vassallo, R.; Martins, F. A low cost sensors approach for accurate vehicle localization and autonomous driving application. Sensors
**2017**, 17, 2359. [Google Scholar] [CrossRef] [PubMed] - Kempf, J.; Arkko, J.; Beheshti, N.; Yedavalli, K. Thoughts on reliability in the internet of things. In Interconnecting Smart Objects with the Internet Workshop; Internet Architecture Board: Boston, MA, USA, 2011; Volume 1, pp. 1–4. [Google Scholar]
- Ahmad, M. Reliability models for the internet of things: A paradigm shift. In Proceedings of the 2014 IEEE International Symposium on Software Reliability Engineering Workshops, Naples, Italy, 3–6 November 2014; pp. 52–59. [Google Scholar]
- Xiao, L.; Wang, R.; Dai, B.; Fang, Y.; Liu, D.; Wu, T. Hybrid conditional random field based camera-LiDAR fusion for road detection. Inf. Sci.
**2018**, 432, 543–558. [Google Scholar] [CrossRef] - Zeng, Y.; Yu, H.; Dai, H.; Song, S.; Lin, M.; Sun, B.; Jiang, W.; Meng, M. An improved calibration method for a rotating 2D LiDAR system. Sensors
**2018**, 18, 497. [Google Scholar] [CrossRef] [PubMed] - Bein, D.; Jolly, V.; Kumar, B.; Latifi, S. Reliability modeling in wireless sensor networks. Int. J. Inf. Technol.
**2005**, 11, 1–8. [Google Scholar] - AboElFotoh, H.M.F.; Iyengar, S.S.; Chakrabarty, K. Computing reliability and message delay for cooperative wireless distributed sensor networks subject to random failures. IEEE Trans. Reliab.
**2005**, 54, 145–155. [Google Scholar] [CrossRef] - Hu, S.; Li, Z.; Zhang, Z.; He, D.; Wimmer, M. Efficient tree modeling from airborne LiDAR point clouds. Comput. Graph.
**2017**, 67, 1–13. [Google Scholar] [CrossRef] - Castaño, F.; Beruvides, G.; Haber, R.; Artuñedo, A. Obstacle recognition based on machine learning for on-chip LiDAR sensors in a cyber-physical system. Sensors
**2017**, 17, 2109. [Google Scholar] [CrossRef] [PubMed] - Shi, B.; Han, L.; Yan, H. Adaptive clustering algorithm based on knn and density. Pattern Recognit. Lett.
**2018**, 104, 37–44. [Google Scholar] [CrossRef] - Zhang, S.; Cheng, D.; Deng, Z.; Zong, M.; Deng, X. A novel knn algorithm with data-driven k parameter computation. Pattern Recognit. Lett.
**2017**. [Google Scholar] [CrossRef] - Połap, D.; Kęsik, K.; Książek, K.; Woźniak, M. Obstacle detection as a safety alert in augmented reality models by the use of deep learning techniques. Sensors
**2017**, 17, 2803. [Google Scholar] [CrossRef] [PubMed] - Stone, P. Q-learning. In Encyclopedia of Machine Learning and Data Mining; Sammut, C., Webb, G.I., Eds.; Springer: Boston, MA, USA, 2017; p. 1033. [Google Scholar]
- Haber, R.E.; Juanes, C.; del Toro, R.; Beruvides, G. Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process. Comput. Ind.
**2015**, 74, 135–150. [Google Scholar] [CrossRef] - Wang, T.; Qiu, J.; Gao, H.; Wang, C. Network-based fuzzy control for nonlinear industrial processes with predictive compensation strategy. IEEE Trans. Syst. Man Cybern. Syst.
**2017**, 47, 2137–2147. [Google Scholar] [CrossRef] - Tian, E.; Yue, D. Decentralized fuzzy H∞ filtering for networked interconnected systems under communication constraints. Neurocomputing
**2016**, 185, 28–36. [Google Scholar] [CrossRef] - Michel, O. Cyberbotics Ltd. Webots™: Professional mobile robot simulation. Int. J. Adv. Robot. Syst.
**2004**, 1, 5. [Google Scholar] [CrossRef] - Fe, I.L.; Beruvides, G.; Quiza, R.; Haber, R.; Rivas, M. Automatic selection of optimal parameters based on simple soft computing methods. A case study on micro-milling processes. IEEE Trans. Ind. Inform.
**2018**. [Google Scholar] [CrossRef] - Alique, A.; Haber, R.E.; Haber, R.H.; Ros, S.; Gonzalez, C. Neural network-based model for the prediction of cutting force in milling process. A progress study on a real case. In Proceedings of the IEEE International Symposium on Intelligent Control, Patras, Greece, 17–19 June 2000; pp. 121–125. [Google Scholar]
- Haber, R.E.; Alique, J.R. Nonlinear internal model control using neural networks: An application for machining processes. Neural Comput. Appl.
**2004**, 13, 47–55. [Google Scholar] [CrossRef] - Aziz, S.; Mohamed, E.A.; Youssef, F. Traffic sign recognition based on multi-feature fusion and elm classifier. Procedia Comput. Sci.
**2018**, 127, 146–153. [Google Scholar] [CrossRef] - Khaldi, B.; Harrou, F.; Cherif, F.; Sun, Y. Self-organization in aggregating robot swarms: A dw-knn topological approach. Biosystems
**2018**, 165, 106–121. [Google Scholar] [CrossRef] [PubMed] - Beruvides, G.; Castaño, F.; Haber, R.E.; Quiza, R.; Villalonga, A. Coping with complexity when predicting surface roughness in milling processes: Hybrid incremental model with optimal parametrization. Complexity
**2017**, 2017. [Google Scholar] [CrossRef] - Penedo, F.; Haber, R.E.; Gajate, A.; Del Toro, R.M. Hybrid incremental modeling based on least squares and fuzzy k-nn for monitoring tool wear in turning processes. IEEE Trans. Ind. Inform.
**2012**, 8, 811–818. [Google Scholar] [CrossRef][Green Version] - Seel, N.M. Greedy q-learning. In Encyclopedia of the Sciences of Learning; Springer: Boston, MA, USA, 2012; p. 1388. [Google Scholar]
- Beruvides, G.; Juanes, C.; Castaño, F.; Haber, R.E. A self-learning strategy for artificial cognitive control systems. In Proceedings of the 2015 IEEE International Conference on Industrial Informatics, Búzios, Brazil, 3–5 June 2015; pp. 1180–1185. [Google Scholar]
- Chincoli, M.; Liotta, A. Self-learning power control in wireless sensor networks. Sensors
**2018**, 18, 375. [Google Scholar] [CrossRef] [PubMed] - Artuñedo, A.; del Toro, R.; Haber, R. Consensus-based cooperative control based on pollution sensing and traffic information for urban traffic networks. Sensors
**2017**, 17, 953. [Google Scholar] [CrossRef] [PubMed] - Zhou, K.; Hou, Q.; Wang, R.; Guo, B. Real-time kd-tree construction on graphics hardware. ACM Trans. Graph.
**2008**, 27, 1–11. [Google Scholar] - Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 2–4 August 1996; AAAI Press: Portland, Oregon, 1996; pp. 226–231. [Google Scholar]
- Lisitsin, V. Spatial data analysis of mineral deposit point patterns: Applications to exploration targeting. Ore Geol. Rev.
**2015**, 71, 861–881. [Google Scholar] [CrossRef] - De Smith, M.J.; Goodchild, M.F.; Longley, P. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools; Troubador Publishing Ltd.: Leicester, UK, 2007. [Google Scholar]
- Burt, J.E.; Barber, G.M.; Rigby, D.L. Elementary Statistics for Geographers; Guilford Press: New York, NY, USA, 2009. [Google Scholar]
- Premebida, C.; Ludwig, O.; Nunes, U. Exploiting LiDAR-based features on pedestrian detection in urban scenarios. In Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA, 4–7 October 2009; pp. 1–6. [Google Scholar]
- Maalek, R.; Sadeghpour, F. Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios. Autom. Constr.
**2016**, 63, 12–26. [Google Scholar] [CrossRef]

**Figure 1.**Conceptual design of self-tuning method. Iteration between IoT assessment framework and supervisor node controller.

**Figure 4.**(

**a**) Aerial view of simulated 3D scenario in Webots automobile; (

**b**) Vehicle model with sensors incorporated; (

**c**) Image captured by the camera and object detection procedure; (

**d**) Point cloud projection from the LiDAR sensor over objects once detected.

**Figure 5.**Prediction error behavior of the model library in the localization of obstacles by LiDAR point clouds.

**Figure 6.**Side (

**a**); front (

**b**); plan (

**c**) and rear view (

**d**) of on-board IoT sensory system setup in a vehicle model.

**Figure 7.**Flow diagram of the self-tuning method for the IoT sensor dynamic obstacle detection scenario.

Detection Threshold (γ) Ranges | Rewards for Number of LiDARs | ||
---|---|---|---|

1 | 3 | 5 | |

0–1 | 1.0 | 0.9 | 0.85 |

1–5 | 0.7 | 0.6 | 0.5 |

5–10 | 0.35 | 0.3 | 0.25 |

+10 | 0.15 | 0.1 | 0 |

Specifications | Ibeo Lux 4 Layers | Specifications | Bumblebee 2 1394a |
---|---|---|---|

Localization | Bottom frontal | Localization | Front top |

Horizontal field | 120 deg. (35 to −50 deg.) | Size resolution max. | 1034 × 776 pixels |

Horizontal step | 0.125 deg. | Pixel resolution | 4.65 µm square pixels |

Vertical field | 3.2 deg. | Focal lengths | 3.8 mm |

Vertical step | 0.8 deg. | Aperture | Focal length/2.0 |

Range | 200 m | Horizontal Field of View | 66° |

Update frequency | 12.5 Hz | Frame rates | 20 FPS |

Models | Correlation Coefficient (R^{2}) | |
---|---|---|

DMRS | MRSE | |

MLP | 0.8668 | 0.8670 |

kNN | 0.9355 | 0.9355 |

Linear Regression | 0.4841 | 0.4858 |

Sensor | Model | Localization (m) |
---|---|---|

3D Stereo Camera | Bumblebee 2 | (0.0, 2.04, 1.2) |

LiDAR 0 | Ibeo Lux 4 layers | (0.0, 3.635, 0.5) |

LiDAR 1 | Ibeo Lux 4 layers | (−0.70, 3.64, 0.5) |

LiDAR 2 | Ibeo Lux 4 layers | (0.70, 3.64, 0.5) |

LiDAR 3 | Ibeo Lux 4 layers | (−0.55, 2.04, 1.2) |

LiDAR 4 | Ibeo Lux 4 layers | (0.55, 2.04, 1.2) |

**Table 5.**Behavior of the correlation (R

^{2}) of each type of model according to the number of LiDAR sensors used at any one given time.

Techniques | Model Correlation (R^{2}) | |||||
---|---|---|---|---|---|---|

1 LiDAR | 3 LiDAR | 5 LiDAR | ||||

DRMS | MRSE | DRMS | MRSE | DRMS | MRSE | |

ANN | 0.8190 | 0.8185 | 0.8949 | 0.9167 | 0.8263 | 0.8279 |

kNN | 0.8184 | 0.8219 | 0.9868 | 0.9871 | 0.8893 | 0.8909 |

Regression | 0.7317 | 0.7300 | 0.7572 | 0.7614 | 0.7269 | 0.7307 |

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**MDPI and ACS Style**

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.
https://doi.org/10.3390/s18051508

**AMA Style**

Castaño F, Beruvides G, Villalonga A, Haber RE. Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models. *Sensors*. 2018; 18(5):1508.
https://doi.org/10.3390/s18051508

**Chicago/Turabian Style**

Castaño, Fernando, Gerardo Beruvides, Alberto Villalonga, and Rodolfo E. Haber. 2018. "Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models" *Sensors* 18, no. 5: 1508.
https://doi.org/10.3390/s18051508