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. Sensors2017, 17, 2109.
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.
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. Sensors2017, 17, 2109.
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.
Abstract
Collision avoidance is an important feature in advanced driver-assistance systems, aiming at providing correct, timely and reliable warnings before an imminent collision (objects, vehicles, pedestrians, etc.). A co-simulation framework is proposed in this paper to address the design and evaluation of collision avoidances in a cyber-physical system. The co-simulation framework is supported on the interaction between SCANeR and Matlab/Simulink. From the best of authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip LIDAR sensors in a cyber-physical system (CPS) considering traffic scenarios is presented. The CPS is designed and implemented in SCANeR. Secondly, an obstacle recognition library with three specific Artificial Intelligence-based methods is also designed based on sensory information database provided by SCANeR. Three methods for collision avoidance detection are considered, i.e.; a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods for detecting obstacles before different weather conditions is done with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and fog conditions, the support vector machine in rainy and self-organized map in snowy conditions.
Copyright:
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.
Received:
28 August 2017
Commenter:
Javier Gonzalo
The commenter has declared there is no conflict of interests.
Comment:
The work is highly topical and shows satisfactory results in the recognition of obstacles based on data provided by a network of virtual sensors. In addition, evaluations of the performance of the system are presented for different climatological conditions.
Commenter:
The commenter has declared there is no conflict of interests.
Commenter: Javier Gonzalo
The commenter has declared there is no conflict of interests.