Vehicle Dynamics Estimation Using Kalman Filters

Venhovens, Paul; Naab, Karl

doi:10.1076/vesd.32.2.171.2088

Cited by 153 publications

(66 citation statements)

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“…However this did not capture the roll state of the vehicle and the method did not compensate the vehicle model uncertainties. Similar with [6], Venhovens and Naab [7] proposed a Kalman filter for lateral state estimation for BMW Driver Assistance Systems, but this paper has similar shortcomings.…”

Section: Introductionmentioning

confidence: 94%

Comparison between Kalman Filter and Robust Filter for Vehicle Handling Dynamics State Estimation

Satria¹,

Best²

2002

SAE Technical Paper Series

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Section: Introductionmentioning

confidence: 94%

Comparison between Kalman Filter and Robust Filter for Vehicle Handling Dynamics State Estimation

Satria¹,

Best²

2002

SAE Technical Paper Series

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“…However, as raw measurements are filtered, such jumps do not occur: even if the raw measurement jumps, it will be smoothed by a filter (e.g. a Kalman Filter, Venhovens and Naab, 1999;Bar-Shalom et al, 2004). Containing this information, the PDF in time step T is located around the position of the measurement in the previous time step T −1 .…”

Section: Choice Of the State Descriptionmentioning

confidence: 99%

Virtual sensor models for real-time applications

et al. 2016

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Abstract. Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensormodel, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.

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“…Earlier work on observers for estimation of lateral velocity is mainly based on linear or quasi-linear techniques, for example [4,22,20,3]. A nonlinear observer linearizing the observer error dynamics is proposed in [13,14].…”

Section: Introductionmentioning

confidence: 99%