Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite sy...

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Vydané v:IEEE sensors journal Ročník 21; číslo 19; s. 21675 - 21687
Hlavní autori: Liu, Wei, Xia, Xin, Xiong, Lu, Lu, Yishi, Gao, Letian, Yu, Zhuoping
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Automated vehicle
Automation
Delays
Dynamic control
Estimation
Global navigation satellite system
Grey prediction
Inertial platforms
information fusion
Kalman filters
Lane changing
low sampling rate
measurement signal delay
Observers
Pitch (inclination)
Prediction algorithms
Rolling motion
Sideslip
Signal delay
Signal measurement
Smoothing methods
Steering
Velocity errors
Wheels
ISSN:1530-437X, 1558-1748
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Popis
Shrnutí:Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3059050