Preprint Article Version 1 This version not peer reviewed

Co-simulation Framework for on-Chip LIDAR Sensors in a Cyber-physical System

Version 1 : Received: 3 August 2017 / Approved: 4 August 2017 / Online: 4 August 2017 (14:13:13 CEST)

A peer-reviewed article of this Preprint also exists.

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. Sensors 2017, 17, 2109.

Journal reference: Sensors 2017, 17, 2109
DOI: 10.3390/s17092109

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.

Subject Areas

sensor-in-the-loop; co-simulation framework; virtual CPS; on-chip LiDAR; obstacle recognition library

Readers' Comments and Ratings (2)

Comment 1
Received: 12 August 2017
Commenter: Alberto Villalonga
The commenter has declared there is no conflict of interests.
Comment: It is a good paper especially because of its topicality and its main contributions.
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Comment 2
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.
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