Sideslip angle estimation is vital to the safety and active control of autonomous vehicles. In this paper, an innovative vehicle kinematic-based sideslip angle estimation method is proposed. The method is built on multi-sensor fusion, which fuses the information of inertial measurement unit, global navigation satellite system (GNSS), and onboard sensors to continuously estimate the attitude and velocity of the vehicle and thus obtain the sideslip angle. The invariant extended Kalman filter framework is adopted and the left-invariant form is derived to implement the GNSS measurement update. In order to solve the unobservability problem when only a single GNSS receiver is equipped, the vehicle motion constraint is introduced into the filter to improve the accuracy of heading angle estimation. The method is validated by field test and the results show that the method is robust in terms of converge efficiency. Furthermore, the sideslip angle estimation accuracy is satisfactory with the average absolute error less than 0.25°, which meets the active safety control requirements for autonomous driving.
YurtseverELambertJCarballoA, et al. A survey of autonomous driving: common practices and emerging technologies. IEEE Access2020; 8: 58443–58469.
2.
Van ZantenAT. Bosch ESP systems: 5 years of experience. SAE trans2000; 109: 428–436.
3.
LiLJiaGSongJ, et al. Progress on vehicle dynamics stability control system. Chin J Mech Eng2013; 49(24): 95–107.
4.
HuCWangRYanF, et al. Should the desired heading in path following of autonomous vehicles be the tangent direction of the desired path?IEEE Trans Intell Transp Syst2015; 16(6): 3084–3094.
5.
GonzalezDPerezJMilanesV, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst2016; 17(4): 1135–1145.
6.
ChengSLiLChenJ. Fusion algorithm design based on adaptive SCKF and integral correction for side-slip angle observation. IEEE Trans Ind Electron2018; 65(7): 5754–5763.
7.
ChindamoDLenzoBGadolaM. On the vehicle sideslip angle estimation: a literature review of methods, models, and innovations. Appl Sci2018; 8(3): 355.
8.
NoviTCapitaniRAnnicchiaricoC. An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements. Proc IMechE, Part D: J Automob Eng2019; 233(7): 1864–1878.
9.
WeiWShaoyiBLanchunZ, et al. Vehicle sideslip angle estimation based on general regression neural network. Math Probl Eng2016; 2016: 1–7.
10.
MelziSSabbioniE. On the vehicle sideslip angle estimation through neural networks: numerical and experimental results. Mech Syst Signal Process2011; 25(6): 2005–2019.
11.
XiongLXiaXLuY, et al. IMU-based automated vehicle body sideslip angle and attitude estimation aided by GNSS using parallel adaptive Kalman filters. IEEE Trans Vehicular Technol2020; 69(10): 10668–10680.
12.
ChangXHLiuY. Robust H∞filtering for vehicle sideslip angle with quantization and data dropouts. IEEE Trans Vehicular Technol2020; 69(10): 10435–10445.
13.
GuoJLuoYLiK, et al. Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation. Mech Syst Signal Process2018; 105: 183–199.
14.
LiaoYWBorrelliF. An adaptive approach to real-time estimation of vehicle sideslip, road bank angles, and sensor bias. IEEE Trans Vehicular Technol2019; 68(8): 7443–7454.
15.
TakikawaKAtsumiYTakanoseA, et al. Vehicular trajectory estimation utilizing slip angle based on GNSS doppler/IMU. ROBOMECH J2021; 8(1): 1–11.
16.
PacejkaHBBakkerE. The magic formula tyre model. Veh Syst Dyn1992; 21(sup001): 1–18.
17.
SelmanajDCornoMPanzaniG, et al. Robust vehicle sideslip estimation based on kinematic considerations. IFAC-PapersOnLine2017; 50(1): 14855–14860.
18.
WangNSunZZhengZ, et al. Finite-time sideslip observer-based adaptive fuzzy path-following control of underactuated marine vehicles with time-varying large sideslip. Int J Fuzzy Syst2018; 20(6): 1767–1778.
19.
BevlyDMRyuJGerdesJC. Integrating ins sensors with GPS measurements for continuous estimation of vehicle sideslip, roll, and tire cornering stiffness. IEEE Trans Intell Transp Syst2006; 7(4): 483–493.
20.
XiaXXiongLLuY, et al. Vehicle sideslip angle estimation by fusing inertial measurement unit and global navigation satellite system with heading alignment. Mech Syst Signal Process2021; 150: 107290.
21.
LiXXuNLiQ, et al. A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches. Proc IMechE, Part D: J Automob Eng2020; 234(7): 1930–1943.
22.
WuZYaoMMaH, et al. Improving accuracy of the vehicle attitude estimation for low-cost INS/GPS integration aided by the GPS-measured course angle. IEEE Trans Intell Transp Syst2013; 14(2): 553–564.
23.
ZhuFHuZLiuW, et al. Dual-antenna GNSS integrated with mems for reliable and continuous attitude determination in challenged environments. IEEE Sens J2019; 19(9): 3449–3461.
24.
BarrauABonnabelS. Three examples of the stability properties of the invariant extended Kalman filter. IFAC-PapersOnLine2017; 50(1): 431–437.
25.
BrossardMBarrauABonnabelS. RINS-W: robust inertial navigation system on wheels. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp.2068–2075. Macau, China: IEEE. DOI:10.1109/IROS40897.2019.8968593.
26.
BarrauABonnabelS. The invariant extended Kalman filter as a stable observer. IEEE Trans Automat Contr2017; 62(4): 1797–1812.
27.
HartleyRGhaffariMEusticeRM, et al. Contact-aided invariant extended Kalman filtering for robot state estimation. Int J Rob Res2020; 39(4): 402–430.
28.
KoNYYounWChoiIH, et al. Features of invariant extended Kalman filter applied to unmanned aerial vehicle navigation. Sensors2018; 18(9): 2855.
29.
ForsterCCarloneLDellaertF, et al. On-manifold preintegration for real-time visual-inertial odometry. IEEE Trans Robot2017; 33(1): 1–21.
30.
GrovesPD. Principles of GNSS, inertial, and multisensor integrated navigation systems, 2nd edition [Book review]. IEEE Aerosp Electron Syst Mag2015; 30(2): 26–27.
31.
Goshen-MeskinDBar-ItzhackIY. Observability analysis of piece-wise constant systems. I. Theory. IEEE Trans Aerosp Electron Syst1992; 28(4): 1056–1067.
32.
BrossardMBarrauABonnabelS. RINS-W: robust inertial navigation system on wheels. arXiv preprint arXiv:1903022102019E.
33.
JoKChuKLeeK, et al. Integration of multiple vehicle models with an IMM filter for vehicle localization. In 2010 IEEE intelligent vehicles symposium, pp.746–751. La Jolla, CA: IEEE.
