Advertisement

Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks

  • Veit LeonhardtEmail author
  • Gerd Wanielik
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

The work presented aims at an early and reliable prediction of lane change maneuvers intended by the driver. For that purpose, an artificial neural network is proposed fusing features modeling the environmental situation that influences the formation of intentions, the gaze behavior of the driver preparing an intended maneuver and the movement of the vehicle. The sensor data required are provided by a multisensor setup comprising automotive radar and camera sensors. The whole prediction algorithm was put into practice as a real-time application and was integrated in a test vehicle. With this system, a naturalistic driving study was conducted on urban roads. The naturalistic driving data obtained were finally used for the parametrization of the algorithm by means of machine learning and for the evaluation of the prediction performance of the algorithm, respectively.

Keywords

Lane change prediction Intention recognition Maneuver prediction Sensor data fusion Neural networks Machine learning Naturalistic driving data Driver intention Driver monitoring Situation assessment 

Notes

Acknowledgements

This work bases on results of the research project UR:BAN Internet Presence (2017). With its 30 partners, it aimed at developing user-oriented assistance systems and network management in urban space. It was supported by the Federal Ministry of Economics and Technology on the basis of a decision by the German Bundestag.

References

  1. Bengler K, Drüke J, Hoffmann S, Manstetten D, Neukum A (2017) UR:BAN human factors in traffic—approaches for safe, efficient and stress-free urban traffic. In: Leonhardt V, Pech T, Wanielik G (eds) Chapter fusion of driver behaviour analysis and situation assessment for probabilistic driving manoeuvre prediction. Springer Vieweg, Heidelberg. ISBN 978-3-658-15417-2Google Scholar
  2. Berndt H, Dietmayer K (2009) Driver intention inference with vehicle onboard sensors. In: Proceedings of the IEEE international conference on vehicular electronics and safety (ICVES), pp. 102–107Google Scholar
  3. Doshi A, Trivedi MM (2009) On the roles of eye gaze and head dynamics in predicting driver’s intent to change lanes. IEEE Trans Intell Transp Syst 10(3):453–462CrossRefGoogle Scholar
  4. Henning M J (2010) Preparation for lane change manoeuvres: behavioural indicators and underlying cognitive processes, Ph.D. dissertation, Chemnitz University of Technology, ChemnitzGoogle Scholar
  5. Kuge N, Yamamura T, Shimoyama O, Liu A (2000) A driver behavior recognition method based on a driver model framework, SAE Technical Paper 2000-01-0349Google Scholar
  6. Leonhardt V, Wanielik G (2017) Feature evaluation for lane change prediction based on driving situation and driver behavior. In: Proceedings of the IEEE international conference on information fusionGoogle Scholar
  7. Leonhardt V, Pech T, Wanielik G (2016) Data fusion and assessment for maneuver prediction including driving situation and driver behavior. In: Proceedings of the IEEE international conference on information fusion, pp. 1702–1708Google Scholar
  8. Lethaus F, Rataj J (2007) Do eye movements reflect driving manoeuvres? Intell Transport Syst (IET) 1(3):199–204CrossRefGoogle Scholar
  9. Lethaus F, Baumann MRK, Köster F, Lemmer K (2011) Using pattern recognition to predict driver intention. Springer, HeidelbergGoogle Scholar
  10. McCall J C, Trivedi M M, Wipf D, Rao B (2005) Lane change intent analysis using robust operators and sparse Bayesian learning. In: IEEE computer society conference on computer vision and pattern recognition (CVPR)—Workshops, pp. 59–59Google Scholar
  11. Oliver N, Pentland A P (2000) Graphical models for driver behavior recognition in a SmartCar. In: Proceedings of the IEEE intelligent vehicles symposium (IV), pp. 7–12Google Scholar
  12. UR: BAN Internet Presence (2007) “http://urban-online.org/de/urban.html
  13. Schroven F, Giebel T (2008) Fahrerintentionserkennung für Fahrerassistenzsysteme. In: Proceedings of 24. VDI/VW-Gemeinschaftstagung—Integrierte Sicherheit und Fahrerassistenzsysteme, Wolfsburg Bd. VDI-Berichte, VDI Verlag, DüsseldorfGoogle Scholar
  14. Statistisches Bundesamt (2016) Verkehr—Verkehrsunfälle 2014, Fachserie 8, Reihe 7Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Chemnitz University of TechnologyChemnitzGermany

Personalised recommendations