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2020 | OriginalPaper | Chapter

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

Authors : Yuran Liang, Steffen Müller, Daniel Rolle, Dieter Ganesch, Immanuel Schaffer

Published in: 10th International Munich Chassis Symposium 2019

Publisher: Springer Fachmedien Wiesbaden

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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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Metadata
Title
Vehicle side-slip angle estimation with deep neural network and sensor data fusion
Authors
Yuran Liang
Steffen Müller
Daniel Rolle
Dieter Ganesch
Immanuel Schaffer
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-658-26435-2_15

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