Functional Decomposition of Lidar Sensor Systems for Model Development

  • Philipp RosenbergerEmail author
  • Martin Holder
  • Marc René Zofka
  • Tobias Fleck
  • Thomas D’hondt
  • Benjamin Wassermann
  • Juraj Prstek


In this chapter, results of the lidar sensor modeling workgroup within ENABLE-S3 are presented. The main objective is to describe the commonly agreed general functional blocks and interfaces of lidar sensor systems for object detection. Having the in the following described interfaces at hand, requirements for the generation of synthetic lidar data at specific interfaces can be formulated and modeling as well as verification and validation of the simulation can be performed.


  1. 1.
    Hanke, T., Hirsenkorn, N., van Driesten, C., Garcia-Ramos, P., Schiementz, M., Schneider, S.K.: Open simulation interface – a generic interface for the environment perception of automated driving functions in virtual scenarios. (2017). Accessed 19 Dec 2018
  2. 2.
    Pegasus Research Project: Accessed 16 Dec 2018
  3. 3.
    Gotzig, H., Geduld, G.: Automotive LIDAR. In: Winner, H., et al. (eds.) Handbook of Driver Assistance Systems. Springer International Publishing, Cham (2016)Google Scholar
  4. 4.
    Weitkamp, C.: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, p. 7. Springer Science & Business, New York (2006)Google Scholar
  5. 5.
    Ibeo Automotive Systems GmbH: ibeo LUX 2010, Laserscanner manual, version 1.6 (2014)Google Scholar
  6. 6.
    Pfotzer, L., Oberländer, J., Rönnau, A., Dillmann, R.: Development and calibration of KaRoLa, a compact, high-resolution 3D laser scanner, SSRR 2014, pp. 1–6Google Scholar
  7. 7.
    Davis, S., Rommel, S., Gann, D., Luey, B., Gamble, J., Ziemkiewcz, M., Anderson, M.: A lightweight, rugged, solid state laser radar system enabled by non-mechanical electro-optic beam steerers. In: LADAR Conference, 2016Google Scholar
  8. 8.
    Wang, Y., Kyoungsik, Y., Wu, M.: MEMS optical phased array for lidar. In: 21st Microoptics Conference, 2016Google Scholar
  9. 9.
    Suni, P., Bowers, J., Coldren, L., Ben Yoo, S.: Photonic integrated circuits for ceherent lidar. In: 18th Coherent Laser Radar Conference, 2016Google Scholar
  10. 10.
    Rosenberger, P., Holder, M., Zirulnik, M., Winner, H.: Analysis of real world sensor behavior for rising fidelity of physically based lidar sensor models. In: 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, pp. 611–616 (2018)Google Scholar
  11. 11.
    Amersbach, C., Winner, H.: Functional decomposition: an approach to reduce the approval effort for highly automated driving. In: 8. Tagung Fahrerassistenz, Einführung hochautomatisiertes Fahren, 2017Google Scholar
  12. 12.
    Nguyen, A., Le, B.: 3d point cloud segmentation: a survey. In: IEEE Conference on Robotics, Automation and Mechatronics, RAM – Proceedings, pp. 225–230 (2013)Google Scholar
  13. 13.
    Schreier, M.: Bayesian environment representation, prediction, and criticality assessment for driver assistance systems. PhD Thesis, Technische Universität Darmstadt, Darmstadt (2016)Google Scholar
  14. 14.
    Granström, K., Baum, M.: Extended object tracking: Introduction, overview and applications. CoRR, vol. abs/1604.00970 (2016)Google Scholar
  15. 15.
    Deshpande, S., Muron, W., Cai, Y.: Chapter 3: Vehicle classification. In: Loce, R.P., Bala, R., Trivedi, M. (eds.) Computer Vision and Imaging in Intelligent Transportation Systems, pp. 47–79. Wiley, Hoboken, NJ (2017)CrossRefGoogle Scholar
  16. 16.
    Definition of the sensor_msgs::LaserScan message type in the Robot Operating System (ROS). Accessed 08 Oct 2017
  17. 17.
    International Organization for Standardization, ISO 8855:2011: Road vehicles – Vehicle dynamics and road-holding ability – Vocabulary, Geneva, CH. Tech. Rep. ISO 8855:2011, 2011Google Scholar
  18. 18.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), 2011Google Scholar
  19. 19.
    Definition of the sensor_msgs::Pointcloud2 message type in the Robot Operating System (ROS). Accessed 08 Oct 2017
  20. 20.
    Definition of the sensor_msgs::PointField message type in the Robot Operating System (ROS). Accessed 08 Oct 2017
  21. 21.
    Ibeo Automotive Systems GmbH: Ethernet data protocol ibeo LUX and ibeo LUX systems, version 1.36 (2017)Google Scholar
  22. 22.
    Velodyne Lidar, Inc.: User’s Manual and Programing Guide – HDL-32E High definition Lidar SensorGoogle Scholar
  23. 23.
    Douillard, B., et al.: On the segmentation of 3D LIDAR point clouds. In: 2011 IEEE International Conference on Robotics and Automation, Shanghai, pp. 2798–2805 (2011)Google Scholar
  24. 24.
    Continental Engineering Services GmbH: Standardized ARS Interface – Technical Documentation, 2012Google Scholar
  25. 25.
    Zofka, M.R., Essinger, M., Fleck, T., Kohlhaas, R., Zöllner, J.M.: The sleepwalker framework: verification and validation of autonomous vehicles by mixed reality lidar stimulation, SIMPAR 2018, pp. 151–157Google Scholar
  26. 26.
    Viehof, M.: Objektive Qualitätsbewertung von Fahrdynamiksimulationen durch statistische Validierung. PhD Thesis, Technische Universität Darmstadt, Darmstadt (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Philipp Rosenberger
    • 1
    Email author
  • Martin Holder
    • 1
  • Marc René Zofka
    • 2
  • Tobias Fleck
    • 2
  • Thomas D’hondt
    • 3
  • Benjamin Wassermann
    • 4
  • Juraj Prstek
    • 5
  1. 1.Institute of Automotive EngineeringTechnische Universität DarmstadtDarmstadtGermany
  2. 2.Stiftung FZI Forschungszentrum InformatikKarlsruheGermany
  3. 3.Siemens Industry Software NVLeuvenBelgium
  4. 4.TWT GmbH Science & InnovationStuttgartGermany
  5. 5.Valeo Autoklimatizace k.s.PragueCzech Republic

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