A LiDAR Error Model for Cooperative Driving Simulations

Michele Segata, Renato Lo Cigno, Rahul Kumar Bhadani, Matthew Bunting, Jonathan Sprinkle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Cooperative driving and vehicular network simulations have done huge steps toward high realism. They have become essential tools for performance evaluation of any kind of vehicular networking application. Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras. So far, these sensors have been assumed to be ideal, i.e., without any measurement error. This paper analyzes a set of estimated distance traces obtained with a LiDAR sensor and develops a stochastic error model that can be used in cooperative driving simulations. After implementing the model within the Plexe simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.

Original languageEnglish (US)
Title of host publication2018 IEEE Vehicular Networking Conference, VNC 2018
EditorsMate Boban, Hsin-Mu Tsai, Onur Altintas, Chih-Yu Wang, Kate Lin, Taylan Sahin
PublisherIEEE Computer Society
ISBN (Electronic)9781538694282
StatePublished - Jan 28 2019
Event2018 IEEE Vehicular Networking Conference, VNC 2018 - Taipei, Taiwan, Province of China
Duration: Dec 5 2018Dec 7 2018

Publication series

NameIEEE Vehicular Networking Conference, VNC
ISSN (Print)2157-9857
ISSN (Electronic)2157-9865


Conference2018 IEEE Vehicular Networking Conference, VNC 2018
Country/TerritoryTaiwan, Province of China

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Automotive Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Transportation


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