Next Article in Journal / Special Issue
An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network
Previous Article in Journal
A Novel Low-Power-Consumption All-Fiber-Optic Anemometer with Simple System Design
Previous Article in Special Issue
Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(9), 2109; https://doi.org/10.3390/s17092109

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Ctra. Campo Real Km. 0.2, Arganda del Rey 28500, Spain
*
Author to whom correspondence should be addressed.
Received: 3 August 2017 / Revised: 8 September 2017 / Accepted: 12 September 2017 / Published: 14 September 2017
View Full-Text   |   Download PDF [5350 KB, uploaded 14 September 2017]   |  

Abstract

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions. View Full-Text
Keywords: sensor-in-the-loop; co-simulation framework; virtual cyber-physical system; on-chip LiDAR; obstacle recognition library sensor-in-the-loop; co-simulation framework; virtual cyber-physical system; on-chip LiDAR; obstacle recognition library
Figures

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Castaño, F.; Beruvides, G.; Haber, R.E.; Artuñedo, A. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System. Sensors 2017, 17, 2109.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top