Computational Intelligence for Simulating a LiDAR Sensor

Cyber-Physical and Internet-of-Things Automotive Applications
  • Fernando CastañoEmail author
  • Gerardo Beruvides
  • Alberto Villalonga
  • Rodolfo E. Haber


In this chapter, an overview of some of the most commonly computational intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-the-art of computational intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific artificial intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine, and a k-nearest neighbour. The results demonstrate the suitability of using computational intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.


LiDAR model Simulation Obstacle recognition Computational intelligence Artificial intelligence Cyber-Physical Systems Internet-of-Things 



The authors wish to thank the support given by 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, the Netherlands, Slovakia, and Spain.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Fernando Castaño
    • 1
    Email author
  • Gerardo Beruvides
    • 2
  • Alberto Villalonga
    • 3
  • Rodolfo E. Haber
    • 1
  1. 1.Centre for Automation and Robotics (CSIC—UPM)Arganda del ReySpain
  2. 2.Automotive and Industry Lab, Hitachi Europe GmbHSchwaig-OberdingGermany
  3. 3.Technical University of Madrid, UPMMadridSpain

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