Learned steering feel by a neural network for a steer-by-wire system

  • Patrick KrupkaEmail author
  • Paul Lukowicz
  • Christopher Kreis
  • Bastian Boßdorf-Zimmer
Conference paper
Part of the Proceedings book series (PROCEE)


High available steering systems for autonomous driving enable the development of Steer-by-Wire systems. Due to the missing mechanical linkage the steering wheel torque which depends on several inputs with different influences needs to be artificially generated by a Force-Feedback-Actuator.

The present publication describes how machine learning methods and especially artificial neural networks can be used to provide a steering feel for a Steer-by-Wire system. Therefore training data consisting of synthetic driving maneuvers is recorded to train a feedforward neural network. Measurement signals of the driver input and the vehicle reaction were selected to be used as inputs for estimating the steering wheel torque as the output of the model.

Networks of different sizes are trained and evaluated on the basis of their training and test error to examine how complex the model must be to calculate the output sufficiently. To extract more information from the training data sliding window features are used in addition to the current signal values.

A trained network has been integrated into the software of a Steer-by-Wire system in a prototype vehicle to provide the steering wheel torque to the Force-Feedback-Actuator. In this vehicle the steering feel generated by the model could be subjectively evaluated on a test site.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1] Garimella, G., Funke, J., Wang, C., & Kobilarov, M. (2017). Neural Network Modeling for Steering Control of an Autonomous Vehicle. International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC, Canada: IEEE/RSJ.Google Scholar
  2. [2] Goodfellow, I., Bengio , Y., & Courville, A. (2016). Deep Learning. Cambridge, Massachusetts, London, England: The MIT Press.Google Scholar
  3. [3] Jayachandran , J., & Ashok, D. (2016). Neural Network Based Approach for the Generation of Road Feel in a Steer-By-Wire System. Eng. J., vol. 20, no. 5, 49-66.Google Scholar
  4. [4] Kim, P. (2017). MATLAB deep learning : with machine learning, neural networks and artificial intelligence. Berkeley, CA: Apress.Google Scholar
  5. [5] Koch, T. (2010). Untersuchungen zum Lenkgefühl von Steer-by-Wire Lenksystemen. München.Google Scholar
  6. [6] Pfeffer, P. (2013). Lenkungshandbuch. Wiesbaden: Springer Vieweg.Google Scholar
  7. [7] van Ende, K., Schaare, D., Kaste, J., Kücükay, F., Henze, R., & Kallmeyer, F. (2016, 07 03). Practicability study on the suitability of artificial, neural networks for the approximation of unknown steering torques. Vehicle System Dynamics, pp. 1362-1383 .Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

Authors and Affiliations

  • Patrick Krupka
    • 1
    Email author
  • Paul Lukowicz
    • 2
  • Christopher Kreis
    • 1
  • Bastian Boßdorf-Zimmer
    • 1
  1. 1.Volkswagen AGBraunschweigGermany
  2. 2.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany

Personalised recommendations