An adaptive estimation of ground vehicle state with unknown measurement noise

• Autorzy: Wang Yan , Sun Xuan , Cui Dong , Wang Xianfang , Jia Zhijuan , Zhang Zhiguo

Identyfikatory

DOI

10.24425/mms.2024.149705

• Języki publikacji: EN

Abstrakty

EN

Accurate information about the vehicle state such as sideslip angle is critical for both advanced assisted driving systems and driverless driving. These vehicle states are used for active safety control and motion planning of the vehicle. Since these state parameters cannot be directly measured by onboard sensors, this paper proposes an adaptive estimation scheme in case of unknown measurement noise. Firstly, an estimation method based on the bicycle model is established using a square-root cubature Kalman filter (SQCKF), and secondly, the expectation maximization (EM) approach is used to dynamically update the statistic parameters of measurement noise and integrate it into SQCKF to form a new expectation maximization square-root cubature Kalman filter (EMSQCKF) algorithm. Simulations and experiments show that EMSQCKF has higher estimation accuracy under different driving conditions compared to the unscented Kalman filter.

Słowa kluczowe

EN

vehicle state estimation; square-root cubature Kalman filter; measurement noise; expectation-maximization method

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2024
• Tom: Vol. 31, nr 2
• Strony: 383--399
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., wykr., wzory

Twórcy

autor

Wang Yan

• The Hong Kong Polytechnic University, Department of Industrial and Systems Engineering, Hong Kong, China

autor

Sun Xuan

• The Beijing Jiaotong University, School of Traffic and Transportation, Beijing, China

autor

Cui Dong

• CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd., Tianjin 300300, China

autor

Wang Xianfang

• School of Computer Science & Technology, Henan Institute of Technology, Xinxiang 453003, China

autor

Jia Zhijuan

• School of Information Science and Technology, Zhengzhou Normal University, Zhengzhou 450044, Henan, China

autor

Zhang Zhiguo

• School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Bibliografia

• [1] Wang, Y., Hu, J., Wang, F. A., Dong, H., Yan, Y., Ren, Y., & Yin, G. (2022). Tire road friction coefficient estimation: review and research perspectives. Chinese Journal of Mechanical Engineering, 335(6). https://doi.org/10.1186/s10033-021-00675-z
• [2] Wang, Y., Chen, H., Yin, G., Mo, Y., de Boer, N., & Lv., C. (2024). Motion State Estimation of Preceding Vehicles with Packet Loss and Unknown Model Parameters. IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/TMECH.2023.3345956
• [3] Bevly, D. M., Ryu, J., & Gerdes, J. C. (2006). Integrating INS sensors with GPS measurements for continuous estimation of vehicle sideslip, roll, and tire cornering stiffness. IEEE Transactions on Intelligent Transportation Systems, 7(4), 483-493. https://doi.org/10.1109/TITS.2006.883110
• [4] Bevly, D. M., Gerdes, J. C., & Wilson, C. (2002). The use of GPS-based velocity measurements for measurement of sideslip and wheel slip. Vehicle System Dynamics, 38(2), 127-147. https://doi.org/10.1076/vesd.38.2.127.5619
• [5] Selmanaj, D., Corno, M., Panzani, G., & Savaresi, S. M. (2017). Vehicle sideslip estimation: A kinematic based approach. Control Engineering Practice, 67, 1-12. https://doi.org/10.1016/j.conengprac.2017.06.013
• [6] Leung, K. T., Whidborne, J. F., Purdy, D., & Dunoyer, A. (2010). A review of ground vehicle dynamic state estimations utilizing GPS/INS. Vehicle System Dynamics, 49(1-2), 29-58. https://doi.org/10.1080/00423110903406649
• [7] Nam, K., Oh, S., Fujimoto, H., & Hori, Y. (2013). Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and Kalman filter approaches. IEEE Transactions on Industrial Electronics, 60(3), 988-1000. https://doi.org/10.1109/TIE.2012.2188874
• [8] Baffet, G., Charara, A., & Lechner, D. (2009). Estimation of vehicle sideslip, tire force and wheel cornering stiffness. Control Engineering Practice, 17(11), 1255-1264. https://doi.org/10.1016/j.conengprac.2009.05.005
• [9] Doumiati, M., Victorino, A. C., Charara, A., & Lechner, D. (2011). Onboard real-time estimation of vehicle lateral tire-road forces and sideslip angle. IEEE-ASME Transactions on Mechatronics, 16(4), 601-614. https://doi.org/10.1109/TMECH.2010.2048118
• [10] Nam, K., Fujimoto, H., & Hori, Y. (2012). Lateral stability control of in-wheel-motor-driven electric vehicles based on sideslip angle estimation using lateral tire force sensors. IEEE Transactions on Vehicular Technology, 61(5), 1972-1985. https://doi.org/10.1109/TVT.2012.2191627
• [11] Li, L., Jia, G., Ran, X., Song, J., & Wu, K. (2014). A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road. Vehicle System Dynamics, 52(2), 280-308. https://doi.org/10.1080/00423114.2013.877148
• [12] Tsunashima, H., Murakami, M., & Miyataa, J. (2006). Vehicle and road state estimation using interacting multiple model approach. Vehicle System Dynamics, 44, 750-758. https://doi.org/10.1080/00423110600885772
• [13] Zhang, F., Wang, Y., Hu, J., Yin, G., Chen, S., Zhang, H., & Zhou, D. (2021). A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter. Electronics, 10(1526). https://doi.org/10.3390/electronics10131526
• [14] Yang, F., Zheng, L., & Wang, J. (2019). Double layer unscented Kalman filter. Acta Automatica Sinica, 45(7), 1386-1391. https://doi.org/10.16383/j.aas.c180349 (in Chinese)
• [15] Wang, Z., Qin, Y., Gu, L., & Dong, M. (2017). Vehicle system state estimation based on adaptive unscented Kalman filtering combining with road classification. IEEE Access, 5, 27786-27799. https://doi.org/10.1109/ACCESS.2017.2771204
• [16] Mishra, A. K., Shimjith, S. R., & Tiwari, A. P. (2019). Adaptive unscented Kalman filtering for reactivity estimation in nuclear power plants. IEEE Transactions on Nuclear Science, 66(12), 2388-2397. https://doi.org/10.1109/TNS.2019.2953196
• [17] Hashemi, E., Kasaiezadeh, A., Khosravani, S., Khajepour, A., Moshchuk, N., & Chen, S.-K. (2016). Estimation of longitudinal speed robust to road conditions for ground vehicles. Vehicle System Dynamics, 54(8), 1120-1146. https://doi.org/10.1080/00423114.2016.1178391
• [18] Heidfeld, H., & Schünemann, M. (2021). Optimization-based tuning of a hybrid UKF state estimator with tire model adaption for an all-wheel-drive electric vehicle. Energies, 14(5), 1396. https://doi.org/10.3390/en14051396
• [19] Chen, J., Song, J., Li, L., Jia, G., Ran, X., & Yang, C. (2016). UKF-based adaptive variable structure observer for vehicle sideslip with dynamic correction. IET Control Theory & Applications, 10, 1641-1652. https://doi.org/10.1049/iet-cta.2015.1030
• [20] Chen, L., Bian, M., Luo, Y., & Li, K. (2016). Real-time identification of the tyre-road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 230, 788-802. https://doi.org/10.1177/0954407015595725
• [21] Jin, X., & Yin, G. (2015). Estimation of lateral tire-road forces and sideslip angle for electric vehicles using interacting multiple model filter approach. Journal of the Franklin Institute, 352, 686-707. https://doi.org/10.1016/j.jfranklin.2014.12.002
• [22] Xin, X., Chen, J., & Zou, J. (2014). Vehicle state estimation using cubature Kalman filter. IEEE 17th International Conference on Computational Science and Engineering, 44-48. https://doi.org/10.1109/CSE.2014.42
• [23] Matsushima, S., Tsujita, T., & Abiko, S. (2020, July). Distance control between an object and an end effector for contactless surface tracking works by a humanoid robot. In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1724-1729). IEEE. https://doi.org/10.1109/AIM43001.2020.9159007
• [24] Zhang, Z., Yin, G., & Wu, Z. (2022). Joint Estimation of Mass and Center of Gravity Position for Distributed Drive Electric Vehicles Using Dual Robust Embedded Cubature Kalman Filter. Sensors, 22(21), 10018. https://doi.org/10.3390/s222410018
• [25] Wang, Y., Geng, K., Xu, L., Ren, Y., Dong, H., & Yin, G. (2020). Estimation of sideslip angle and tire cornering stiffness using fuzzy adaptive robust cubature Kalman filter. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(3), 1451-1462. https://doi.org/10.1109/TSMC.2020.3020562
• [26] Yaming, L., Rongyun, Z., Peicheng, S., Linfeng, Z., Yongle, F., & Yufeng, D. (2022). Distributed Electric Vehicle State Parameter Estimation Based on the ASO-SRGHCKF Algorithm. IEEE Sensors Journal, 22(18), 18780-18792. https://doi.org/10.1109/JSEN.2022.3199488
• [27] Xiong, H., Liu, J., Zhang, R., Zhu, X., & Liu, H. (2019). An accurate vehicle and road condition estimation algorithm for vehicle networking applications. IEEE Access, 7, 17705-17715. https://doi.org/10.1109/ACCESS.2019.2895072
• [28] Hou, S., Xu, W., & Liu, G. (2019). Design of an interacting multiple model-cubature Kalman filter approach for vehicle sideslip angle and tire forces estimation. Mathematical Problems in Engineering. https://doi.org/10.1155/2019/6087450
• [29] Schramm, D., Hiller, M., & Bardini, R. (2018). Single Track Models. In: Vehicle Dynamics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54483-9_10
• [30] Cheng, S., Li, L., & Chen, J. (2017). Fusion algorithm design based on adaptive SCKF and integral correction for side-slip angle observation. IEEE Transactions on Industrial Electronics, 65(7), 5754-5763. https://doi.org/10.1109/TIE.2017.2774771
• [31] Shen C., Zhang Y., Guo X., Chen X., Cao H., Tang, J. Li, & Liu J. (2021). Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter. IEEE Transactions on Industrial Electronics, 68(1), 499-508, https://doi.org/10.1109/TIE.2020.2967671

Uwagi

This work was supported by the Smart Traffic Fund (grants no. PSRI/47/2209/PR, PSRI/53/2210/PR) and supported by the National Natural Science Foundation of China (grant no. 62072157).