Approximating Road Geometry with Multisine Signals for Driver Identification

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The understanding of human responses to visual information in car driving tasks requires the use of system identification tools that put constraints on the design of data collection experiments. Most importantly, multisine perturbation signals are required, including a multisine road geometry, to separately identify the different driver steering responses in the frequency domain. It is as of yet unclear, however, to what extent drivers steer differently along such multisine roads than they do for real roads. This paper presents a method for approximating real-world road geometries with multisine signals, and applies it to a stretch of road used in an earlier investigation into driver steering. In addition, a human-in-the-loop experiment is performed to collect driver steering data for both the realistic real-world road and its multisine approximation. Overall, the analysis of driver performance metrics and driver identification data shows that drivers adopt equivalent control behaviour when steering along both roads. Hence, the use of such multisine approximations allows for the realization of realistic roads and driver behaviour in car driving experiments, in addition to supporting the application of quantitative driver identification techniques for data analysis.

Original languageEnglish
Pages (from-to)341-346
Number of pages6
Issue number19
Publication statusPublished - 2019
Event14th IFAC Symposium on Analysis, Design, and Evaluation of Human Machine Systems, HMS 2019 - Tallinn, Estonia
Duration: 16 Sept 201919 Sept 2019


  • driver modeling
  • driving
  • manual control
  • multisine signals
  • system identification


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