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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Veröffentlicht in:	IEEE sensors journal Jg. 21; H. 19; S. 21675 - 21687
Hauptverfasser:	Liu, Wei, Xia, Xin, Xiong, Lu, Lu, Yishi, Gao, Letian, Yu, Zhuoping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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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Abstract	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.
AbstractList	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.
Author	Lu, Yishi Gao, Letian Liu, Wei Yu, Zhuoping Xia, Xin Xiong, Lu
Author_xml	– sequence: 1 givenname: Wei orcidid: 0000-0003-4251-5793 surname: Liu fullname: Liu, Wei organization: School of Automotive Studies, Tongji University, Shanghai, China – sequence: 2 givenname: Xin orcidid: 0000-0002-5108-7578 surname: Xia fullname: Xia, Xin email: 10xinxia@tongji.edu.cn organization: School of Automotive Studies, Tongji University, Shanghai, China – sequence: 3 givenname: Lu orcidid: 0000-0002-1673-2658 surname: Xiong fullname: Xiong, Lu organization: School of Automotive Studies, Tongji University, Shanghai, China – sequence: 4 givenname: Yishi surname: Lu fullname: Lu, Yishi organization: School of Automotive Studies, Tongji University, Shanghai, China – sequence: 5 givenname: Letian surname: Gao fullname: Gao, Letian organization: School of Automotive Studies, Tongji University, Shanghai, China – sequence: 6 givenname: Zhuoping orcidid: 0000-0002-8775-0052 surname: Yu fullname: Yu, Zhuoping organization: School of Automotive Studies, Tongji University, Shanghai, China
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SubjectTerms	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
Title	Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic
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