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Time-To-Failure Modelling in On-Chip LiDAR Sensors for Automotive Applications †

Fernando Castaño
Gerardo Beruvides
Rodolfo E. Haber
1 and
Alberto Villalonga
Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Arganda del Rey 28500, Spain
Research Center of Advanced and Sustainable Manufacturing, University of Matanzas (UM), Matanzas 44100, Cuba
Author to whom correspondence should be addressed.
Presented at the 5th International Symposium on Sensor Science (I3S 2017), Barcelona, Spain, 27–29 September 2017.
Proceedings 2017, 1(8), 809;
Published: 29 November 2017
In general, when forward-looking on-chip LiDAR is considered, the role of these sensors in vehicle collision avoidance is very important. Therefore, the reliability assessment related to accuracy in obstacle detection from information provided by LiDAR sensors has become a key issue to be researched by the scientific community. The analysis of reliability must be focused on certain critical points such as solution to navigation errors, measurement range error, error in the scanning angle, divergence in the laser, etc. This paper establishes a relationship based on models for obstacle detection and classification in complex traffic scenarios. These models have been generated from data collected, provided by LIDAR sensor models, implemented in a commercial simulation tool such as SCANeR studio. For this, a traffic scenario has been created in this simulation tool. To create models, the proposal combines two widely reported pattern recognition methodologies, including fully flexible Bayesian Networks and k-nearest neighbors algorithm. Subsequently, a comparison is made during a model simulation in a traffic scenario, obtaining very promising results in terms of accuracy based on two merit figures: distance root mean square and mean root square error. Finally, the best results have been reached with k-nearest neighbors algorithm.


The authors wish to thank the Autonomous University of Madrid in the framework of the bilateral initiative with UMCC for research activities. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European project IoSENSE: Flexible FE/BE Sensor Pilot Line for the Internet of Everything. This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 692480. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Germany, Saxony, Austria, Belgium, Netherlands, Slovakia, Spain.

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MDPI and ACS Style

Castaño, F.; Beruvides, G.; Haber, R.E.; Villalonga, A. Time-To-Failure Modelling in On-Chip LiDAR Sensors for Automotive Applications. Proceedings 2017, 1, 809.

AMA Style

Castaño F, Beruvides G, Haber RE, Villalonga A. Time-To-Failure Modelling in On-Chip LiDAR Sensors for Automotive Applications. Proceedings. 2017; 1(8):809.

Chicago/Turabian Style

Castaño, Fernando, Gerardo Beruvides, Rodolfo E. Haber, and Alberto Villalonga. 2017. "Time-To-Failure Modelling in On-Chip LiDAR Sensors for Automotive Applications" Proceedings 1, no. 8: 809.

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