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Vehicle side-slip angle estimation with deep neural network and sensor data fusion

  • Yuran LiangEmail author
  • Steffen Müller
  • Daniel Rolle
  • Dieter Ganesch
  • Immanuel Schaffer
Conference paper
Part of the Proceedings book series (PROCEE)

Abstract

Modern chassis control systems, advanced driver assistance systems (ADAS) and automated driving systems that demand a precise vehicle localization or a reasonable trajectory planning desire a highly accurate and reliable vehicle state estimation. However, the traditional methods such as Kalman and RLS filter, which based on the vehicle dynamic model, mainly rely on the differential equations that approximate the vehicle behaviour in reality [1, 27, 31]. The vehicle dynamics is such a nonlinear and multidimensional system with numerous parameters, which makes it very difficult to adapt the parameters in different situations and figure out appropriate model equations.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

Authors and Affiliations

  • Yuran Liang
    • 1
    Email author
  • Steffen Müller
    • 2
  • Daniel Rolle
    • 1
  • Dieter Ganesch
    • 1
  • Immanuel Schaffer
    • 1
  1. 1.BMW GroupMunichGermany
  2. 2.TU BerlinBerlinGermany

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