Comprehensive Overview and Comparative Assessment of Model-Based Methods for Vehicle State Estimation
DOI:
https://doi.org/10.31181/rme497Keywords:
Vehicle State Estimation, Observer Design, Parameter Estimation, Sensor Fusion, Model-Based ApproachesAbstract
Accurate vehicle state estimation is essential for advanced driver-assistance systems (ADAS) and autonomous driving applications, enabling robust control performance under diverse operating conditions. In this context, it becomes even more critical to design generalizable tools capable of adapting to any vehicle, operating condition, or environment: for this reason, model-based approaches become a true necessity, especially when relying on the outputs of such algorithms in closed-loop control and safety applications, making these methodologies more suitable than purely data-driven approaches. However, in current literature, there exists a variety of model-based estimation strategies with different levels of complexity, accuracy, and computational requirements. This paper reviews approximately 200 studies and presents a comprehensive review of model-based vehicle state estimation techniques, with a focus on their formulation, underlying physical assumptions, and associated trade-offs. Estimation strategies are categorized into pure kinematic, vehicle- and tire-model-based, hybrid, and more complex multibody-model-based approaches, thereby facilitating informed selection of the most suitable method to support design choices based on sensor availability and application requirements. Within each category, a more detailed analysis examines estimation strategies with variable modeling assumptions, observer architectures, and sensor configurations, emphasizing how design choices affect accuracy, robustness to parameter variations, computational cost, and sensitivity to measurement inaccuracies. In contrast to previous surveys, this work provides a structured comparative assessment of all methodologies using specifically defined evaluation criteria, enabling a direct comparison across different estimation approaches and highlighting their respective advantages and limitations. The review concludes by outlining current limitations in the state of the art and identifying promising research directions, including the integration of currently unmodeled physical effects into estimation algorithms, the necessary steps toward simplified calibration and self-calibration procedures even in detailed modeling approaches, and the exploitation of emerging sensor technologies.
References
Acarman, T. (2008). Observation of vehicle states by using steering wheel angle and wheel angular speeds. In 2008 IEEE International Conference on Vehicular Electronics and Safety (pp. 189-194). IEEE. https://doi.org/10.1109/ICVES.2008.4640890
Ahmed, A. A., Barood, F. A. E., & Khalifa, M. S. (2021). Vehicle yaw rate simulation and control based on single-track model. World Journal of Advanced Research and Reviews, 10(01), 019-029. https://doi.org/10.30574/wjarr.2021.10.1.0127
Ahmed, A. A., Santhosh, J., & Aldbea, F. W. (2020). Vehicle dynamics modeling and simulation with control using single track model. In 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) (pp. 1-4). IEEE. https://doi.org/10.1109/WIECON-ECE52138.2020.9397983
Ahmed, S. K., Mohammed, M. G., Abdulqadir, S. O., El‐Kader, R. G. A., El‐Shall, N. A., Chandran, D., Rehman, M. E. U., & Dhama, K. (2023). Road traffic accidental injuries and deaths: A neglected global health issue. Health Science Reports, 6(5), e1240. https://doi.org/10.1002/hsr2.1240
Ahn, Y., Han, S., Kim, G., & Huh, K. (2024). Slope and mass estimation for semi-trailer truck considering pitch angle variations. IEEE Transactions on Intelligent Vehicles. https://doi.org/10.1109/TIV.2024.3418473
Albinsson, A., Bruzelius, F., Jonasson, M., & Jacobson, B. (2014). Tire force estimation utilizing wheel torque measurements and validation in simulations and experiments. In Proceedings of the 12th International Symposium on Advanced Vehicle Control (Vol. 9, pp. 28). https://www.academia.edu/download/79430419/local_203619.pdf
Anderson, R., & Bevly, D. M. (2010). Using GPS with a model-based estimator to estimate critical vehicle states. Vehicle system dynamics, 48(12), 1413-1438. https://doi.org/10.1080/00423110903461347
Antonov, S., Fehn, A., & Kugi, A. (2011). Unscented Kalman filter for vehicle state estimation. Vehicle system dynamics, 49(9), 1497-1520. https://doi.org/10.1080/00423114.2010.527994
Athans, M., & Chang, C.-B. (1976). Adaptive estimation and parameter identification using multiple model estimation algorithm. https://apps.dtic.mil/sti/html/tr/ADA028510/
Bae, H. S., Ryu, J., & Gerdes, J. C. (2001). Road grade and vehicle parameter estimation for longitudinal control using GPS. In Proceedings of the IEEE Conference on Intelligent Transportation Systems (pp. 25-29). http://www-cdr.stanford.edu/dynamic/PATH/Papers/bae_ieee_its2001.pdf
Baffet, G., Charara, A., & Dherbomez, G. (2007). An observer of tire–road forces and friction for active security vehicle systems. IEEE/ASME Transactions on Mechatronics, 12(6), 651-661. https://doi.org/10.1109/TMECH.2007.910099
Baffet, G., Charara, A., & Lechner, D. (2008). Estimation of tire-road forces and vehicle sideslip angle. Advances in robotics, automation and control, 137. https://doi.org/10.5772/5531
Batista, P., Silvestre, C., Oliveira, P., & Cardeira, B. (2010). Accelerometer calibration and dynamic bias and gravity estimation: Analysis, design, and experimental evaluation. IEEE transactions on control systems technology, 19(5), 1128-1137. https://doi.org/10.1109/TCST.2010.2076321
Berntorp, K., Quirynen, R., & Vaskov, S. (2021). Joint tire-stiffness and vehicle-inertial parameter estimation for improved predictive control. In 2021 American Control Conference (ACC) (pp. 186-191). IEEE. https://doi.org/10.23919/ACC50511.2021.9482635
Bersani, M., Vignati, M., Mentasti, S., Arrigoni, S., & Cheli, F. (2019). Vehicle state estimation based on Kalman filters. In 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) (pp. 1-6). IEEE. https://doi.org/10.23919/EETA.2019.8804527
Bevly, D. M. (2004). Global positioning system (GPS): A low-cost velocity sensor for correcting inertial sensor errors on ground vehicles. Journal of dynamic systems, measurement, and control, 126(2), 255-264. https://doi.org/10.1115/1.1766027
Bishop, G., & Welch, G. (2001). An introduction to the kalman filter. Proc of SIGGRAPH, Course, 8(27599-23175), 41. https://www.academia.edu/download/52484748/Kalman_CoursePack_08.pdf
Boada, B. L., Boada, M. J. L., & Zhang, H. (2019). Sensor fusion based on a dual Kalman filter for estimation of road irregularities and vehicle mass under static and dynamic conditions. IEEE/ASME Transactions on Mechatronics, 24(3), 1075-1086. https://doi.org/10.1109/TMECH.2019.2909977
Bonfitto, A., Feraco, S., Amati, N., & Tonoli, A. (2019). Virtual sensing in high-performance vehicles with artificial intelligence. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 59216, pp. V003T001A005). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2019-97906
Canale, M., Fagiano, L., Ruiz, F., & Signorile, M. C. (2008). A study on the use of virtual sensors in vehicle control. In 2008 47th IEEE Conference on Decision and Control (pp. 4402-4407). IEEE. https://doi.org/10.1109/CDC.2008.4739323
Carnier, S., Corno, M., & Savaresi, S. M. (2023). Hybrid kinematic-dynamic sideslip and friction estimation. Journal of dynamic systems, measurement, and control, 145(5), 051004. https://doi.org/10.1115/1.4062159
Channamallu, S. S., Kermanshachi, S., & Pamidimukkala, A. (2023). Impact of autonomous vehicles on traffic crashes in comparison with conventional vehicles. In International Conference on Transportation and Development 2023 (pp. 39-50). https://doi.org/10.1061/9780784484876.005
Chen, T., Cai, Y., Chen, L., & Xu, X. (2023). Sideslip angle fusion estimation method of three-axis autonomous vehicle based on composite model and adaptive cubature Kalman filter. IEEE Transactions on Transportation Electrification, 10(1), 316-330. https://doi.org/10.1109/TTE.2023.3263592
Chen, X., Li, S., Li, L., Zhao, W., & Cheng, S. (2022). Longitudinal-lateral-cooperative estimation algorithm for vehicle dynamics states based on adaptive-square-root-cubature-Kalman-filter and similarity-principle. Mechanical Systems and Signal Processing, 176, 109162. https://doi.org/10.1016/j.ymssp.2022.109162
Cho, W., Yoon, J., Yim, S., Koo, B., & Yi, K. (2009). Estimation of tire forces for application to vehicle stability control. IEEE Transactions on Vehicular Technology, 59(2), 638-649. https://doi.org/10.1109/TVT.2009.2034268
Corno, M., Panzani, G., & Savaresi, S. M. (2015). Single-track vehicle dynamics control: State of the art and perspective. IEEE/ASME Transactions on Mechatronics, 20(4), 1521-1532. https://doi.org/10.1109/TMECH.2014.2382717
Cuadrado, J., Cardenal, J., & Bayo, E. (1997). Modeling and solution methods for efficient real-time simulation of multibody dynamics. Multibody System Dynamics, 1(3), 259-280. https://doi.org/10.1023/A:1009754006096
Cuadrado, J., Dopico, D., Perez, J. A., & Pastorino, R. (2012). Automotive observers based on multibody models and the extended Kalman filter. Multibody System Dynamics, 27(1), 3-19. https://doi.org/10.1007/s11044-011-9251-1
Cuadrado, J., Vilela, D., Iglesias, I., Martín, A., & Peña, A. (2013). A multibody model to assess the effect of automotive motor in-wheel configuration on vehicle stability and comfort. ECCOMAS multibody dynamics. https://www.academia.edu/download/110700636/2013_07_ECCOMAS_AMultibody.pdf
Cuesta, C., Luque, P., & Mántaras, D. A. (2016). State estimation applied to non-explicit multibody models. Nonlinear Dynamics, 86(3), 1673-1686. https://doi.org/10.1007/s11071-016-2985-9
Daiss, A., & Kiencke, U. (1995). Estimation of vehicle speed fuzzy-estimation in comparison with Kalman-filtering. In Proceedings of International Conference on Control Applications (pp. 281-284). IEEE. https://doi.org/10.1109/CCA.1995.555716
Dakhlallah, J., Glaser, S., Mammar, S., & Sebsadji, Y. (2008). Tire-road forces estimation using extended Kalman filter and sideslip angle evaluation. In 2008 American control conference (pp. 4597-4602). IEEE. https://doi.org/10.1109/ACC.2008.4587220
Davoodabadi, I., Ramezani, A. A., Mahmoodi-k, M., & Ahmadizadeh, P. (2014). Identification of tire forces using Dual Unscented Kalman Filter algorithm. Nonlinear Dynamics, 78(3), 1907-1919. https://doi.org/10.1007/s11071-014-1566-z
de Araujo, P. R. M., Elhabiby, M., Givigi, S., & Noureldin, A. (2023). A novel method for land vehicle positioning: Invariant kalman filters and deep-learning-based radar speed estimation. IEEE Transactions on Intelligent Vehicles, 8(9), 4275-4286. https://doi.org/10.1109/TIV.2023.3287790
Ding, N., Chen, W., Zhang, Y., Xu, G., & Gao, F. (2014). An extended Luenberger observer for estimation of vehicle sideslip angle and road friction. International Journal of Vehicle Design, 66(4), 385-414. https://doi.org/10.1504/IJVD.2014.066071
Do, J., Hyun, D., Han, K., & Choi, S. B. (2024). Real-time estimation of longitudinal tire stiffness considering dynamic characteristics of tire. Mechatronics, 98, 103120. https://doi.org/10.1016/j.mechatronics.2023.103120
Doumiati, M., Victorino, A., Charara, A., & Lechner, D. (2009a). Estimation of vehicle lateral tire-road forces: a comparison between extended and unscented Kalman filtering. In 2009 European Control Conference (ECC) (pp. 4804-4809). IEEE. https://doi.org/10.23919/ECC.2009.7075160
Doumiati, M., Victorino, A., Charara, A., & Lechner, D. (2009b). Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation. In 2009 IEEE intelligent vehicles symposium (pp. 901-906). IEEE. https://doi.org/10.1109/IVS.2009.5164399
Doumiati, M., Victorino, A., Charara, A., & Lechner, D. (2011). Estimation of road profile for vehicle dynamics motion: experimental validation. In Proceedings of the 2011 American control conference (pp. 5237-5242). IEEE. https://doi.org/10.1109/ACC.2011.5991595
Doumiati, M., Victorino, A., Lechner, D., Baffet, G., & Charara, A. (2010). Observers for vehicle tyre/road forces estimation: experimental validation. Vehicle system dynamics, 48(11), 1345-1378. https://doi.org/10.1080/00423111003615204
Doumiati, M., Victorino, A. C., Charara, A., & Lechner, D. (2010). 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
Du, M., Zhao, D., Yang, M., & Chen, H. (2020). Nonlinear extended state observer-based output feedback stabilization control for uncertain nonlinear half-car active suspension systems. Nonlinear Dynamics, 100(3), 2483-2503. https://doi.org/10.1007/s11071-020-05638-y
Eric Tseng, H., Xu, L., & Hrovat, D. (2007). Estimation of land vehicle roll and pitch angles. Vehicle system dynamics, 45(5), 433-443. https://doi.org/10.1080/00423110601169713
Farrelly, J., & Wellstead, P. (1996). Estimation of vehicle lateral velocity. In Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Contro (pp. 552-557). IEEE. https://doi.org/10.1109/CCA.1996.558920
Fathy, H. K., Kang, D., & Stein, J. L. (2008). Online vehicle mass estimation using recursive least squares and supervisory data extraction. In 2008 American control conference (pp. 1842-1848). IEEE. https://doi.org/10.1109/ACC.2008.4586760
Feng, S., Li, X., Zhang, S., Jian, Z., Duan, H., & Wang, Z. (2023). A review: State estimation based on hybrid models of Kalman filter and neural network. Systems Science & Control Engineering, 11(1), 2173682. https://doi.org/10.1080/21642583.2023.2173682
Fujii, K. (2006). Proposal of slip ratio observer without detection of vehicle speed for electric vehicle. JIASC2006, 2, 503-506. https://cir.nii.ac.jp/crid/1570009750683188096
Fujii, K., & Fujimoto, H. (2007). Traction control based on slip ratio estimation without detecting vehicle speed for electric vehicle. In 2007 Power conversion conference-Nagoya (pp. 688-693). IEEE. https://doi.org/10.1109/PCCON.2007.373040
Gadola, M., Chindamo, D., Romano, M., & Padula, F. (2014). Development and validation of a Kalman filter-based model for vehicle slip angle estimation. Vehicle system dynamics, 52(1), 68-84. https://doi.org/10.1080/00423114.2013.859281
Galluppi, O., Corno, M., & Savaresi, S. M. (2018). Mixed-kinematic body sideslip angle estimator for high performance cars. In 2018 European Control Conference (ECC) (pp. 941-946). IEEE. https://doi.org/10.23919/ECC.2018.8550619
Goel, R., Tiwari, G., Varghese, M., Bhalla, K., Agrawal, G., Saini, G., Jha, A., John, D., Saran, A., & White, H. (2024). Effectiveness of road safety interventions: An evidence and gap map. Campbell systematic reviews, 20(1), e1367. https://doi.org/10.1002/cl2.1367
González, A., O'brien, E. J., Li, Y.-Y., & Cashell, K. (2008). The use of vehicle acceleration measurements to estimate road roughness. Vehicle system dynamics, 46(6), 483-499. https://doi.org/10.1080/00423110701485050
Grip, H. F., Imsland, L., Johansen, T. A., Fossen, T. I., Kalkkuhl, J. C., & Suissa, A. (2008). Nonlinear vehicle side-slip estimation with friction adaptation. Automatica, 44(3), 611-622. https://doi.org/10.1016/j.automatica.2007.06.017
Grip, H. F., Imsland, L., Johansen, T. A., Kalkkuhl, J. C., & Suissa, A. (2009). Estimation of road inclination and bank angle in automotive vehicles. In 2009 American control conference (pp. 426-432). IEEE. https://doi.org/10.1109/ACC.2009.5159912
GROVES, P. D. (2008). NAVIGATION SYSTEMS. Principes of GNSS, Inertial and Multisensor Integrated, 1-17. http://www.frequencyfinder.org.uk/navbook/History.pdf
Guiggiani, M. (2014). The science of vehicle dynamics. Pisa, Italy: Springer Netherlands, 15, 32. https://doi.org/10.1007/978-3-031-06461-6
Guo, H., Cao, D., Chen, H., Lv, C., Wang, H., & Yang, S. (2018). Vehicle dynamic state estimation: State of the art schemes and perspectives. IEEE/CAA Journal of Automatica Sinica, 5(2), 418-431. https://doi.org/10.1109/JAS.2017.7510811
Han, S., & Huh, K. (2011). Monitoring system design for lateral vehicle motion. IEEE Transactions on Vehicular Technology, 60(4), 1394-1403. https://doi.org/10.1109/TVT.2011.2122312
Hao, S., Luo, P., & Xi, J. (2017). Estimation of vehicle mass and road slope based on steady-state Kalman filter. In 2017 IEEE International Conference on Unmanned Systems (ICUS) (pp. 582-587). IEEE. https://doi.org/10.1109/ICUS.2017.8278412
Hashemi, E., Khosravani, S., Khajepour, A., Kasaiezadeh, A., Chen, S.-K., & Litkouhi, B. (2017). Longitudinal vehicle state estimation using nonlinear and parameter-varying observers. Mechatronics, 43, 28-39. https://doi.org/10.1016/j.mechatronics.2017.02.004
Hashemi, E., Zarringhalam, R., Khajepour, A., Melek, W., Kasaiezadeh, A., & Chen, S.-K. (2017). Real-time estimation of the road bank and grade angles with unknown input observers. Vehicle system dynamics, 55(5), 648-667. https://doi.org/10.1080/00423114.2016.1275706
He, Z., Shi, Q., Wei, Y., Gao, B., Zhu, B., & He, L. (2021). A model predictive control approach with slip ratio estimation for electric motor antilock braking of battery electric vehicle. IEEE Transactions on Industrial Electronics, 69(9), 9225-9234. https://doi.org/10.1109/TIE.2021.3112966
Heidfeld, H., Schünemann, M., & Kasper, R. (2020). UKF-based State and tire slip estimation for a 4WD electric vehicle. Vehicle system dynamics, 58(10), 1479-1496. https://doi.org/10.1080/00423114.2019.1648836
Hong, S., Lee, C., Borrelli, F., & Hedrick, J. K. (2014). A novel approach for vehicle inertial parameter identification using a dual Kalman filter. IEEE Transactions on Intelligent Transportation Systems, 16(1), 151-161. https://doi.org/10.1109/TITS.2014.2329305
Hsiao, T. (2012). Robust estimation and control of tire traction forces. IEEE Transactions on Vehicular Technology, 62(3), 1378-1383. https://doi.org/10.1109/TVT.2012.2230656
Huang, Y., Bao, C., Wu, J., & Ma, Y. (2017). Estimation of sideslip angle based on extended Kalman filter. Journal of Electrical and Computer Engineering, 2017(1), 5301602. https://doi.org/10.1155/2017/5301602
Hyun, M., & Cho, W. (2018). Estimation of road bank angle and vehicle Side slip angle using Bayesian tracking and Kalman filter approach. International Journal of Automotive Technology, 19(6), 993-1000. https://doi.org/10.1007/s12239-018-0096-y
Indu, K., & Aswatha Kumar, M. (2023). Electric vehicle control and driving safety systems: A review. IETE Journal of Research, 69(1), 482-498. https://doi.org/10.1080/03772063.2020.1830862
Jensen, K. M., Santos, I. F., Clemmensen, L. K., Theodorsen, S., & Corstens, H. J. (2022). Mass estimation of ground vehicles based on longitudinal dynamics using loosely coupled integrated navigation system and CAN-bus data with model parameter estimation. Mechanical Systems and Signal Processing, 171, 108925. https://doi.org/10.1016/j.ymssp.2022.108925
Jia, G., Li, L., & Cao, D. (2015). Model-based estimation for vehicle dynamics states at the limit handling. Journal of dynamic systems, measurement, and control, 137(10). https://doi.org/10.1115/1.4030784
Jin, X.-B., Robert Jeremiah, R. J., Su, T.-L., Bai, Y.-T., & Kong, J.-L. (2021). The new trend of state estimation: From model-driven to hybrid-driven methods. Sensors, 21(6), 2085. https://doi.org/10.3390/s21062085
Jin, X., Yang, J., Xu, L., Wei, C., Wang, Z., & Yin, G. (2023). Combined estimation of vehicle dynamic state and inertial parameter for electric vehicles based on dual central difference Kalman filter method. Chinese Journal of Mechanical Engineering, 36(1), 91. https://doi.org/10.1186/s10033-023-00914-5
Jin, X., Yin, G., & Chen, N. (2019). Advanced estimation techniques for vehicle system dynamic state: A survey. Sensors, 19(19), 4289. https://doi.org/10.3390/s19194289
Kamal Mazhar, M., Khan, M. J., Bhatti, A. I., & Naseer, N. (2020). A novel roll and pitch estimation approach for a ground vehicle stability improvement using a low cost IMU. Sensors, 20(2), 340. https://doi.org/10.3390/s20020340
Kang, C. M., Lee, S.-H., & Chung, C. C. (2014). Comparative evaluation of dynamic and kinematic vehicle models. In 53rd IEEE Conference on Decision and Control (pp. 648-653). IEEE. https://doi.org/10.1109/CDC.2014.7039455
Katriniok, A., & Abel, D. (2015). Adaptive EKF-based vehicle state estimation with online assessment of local observability. IEEE transactions on control systems technology, 24(4), 1368-1381. https://doi.org/10.1109/TCST.2015.2488597
Kim, G., Lee, S. Y., Oh, J.-S., & Lee, S. (2021). Deep learning-based estimation of the unknown road profile and state variables for the vehicle suspension system. IEEE Access, 9, 13878-13890. https://doi.org/10.1109/ACCESS.2021.3051619
Kim, J. (2009). Identification of lateral tyre force dynamics using an extended Kalman filter from experimental road test data. Control Engineering Practice, 17(3), 357-367. https://doi.org/10.1016/j.conengprac.2008.08.002
Kim, J. (2010). Effect of vehicle model on the estimation of lateral vehicle dynamics. International Journal of Automotive Technology, 11(3), 331-337. https://doi.org/10.1007/s12239-010-0041-1
Kim, K., Park, H., & Kim, T. (2023). Comparison of performance of predicting the wear amount of tire tread depending on sensing information. Sensors, 23(1), 459. https://doi.org/10.3390/s23010459
Klomp, M., Gao, Y., & Bruzelius, F. (2014). Longitudinal velocity and road slope estimation in hybrid electric vehicles employing early detection of excessive wheel slip. Vehicle system dynamics, 52(sup1), 172-188. https://doi.org/10.1080/00423114.2014.887737
Krishnaswami, V., & Rizzoni, G. (1995). Vehicle steering system state estimation using sliding mode observers. In Proceedings of 1995 34th IEEE Conference on Decision and Control (Vol. 4, pp. 3391-3396). IEEE. https://doi.org/10.1109/CDC.1995.479012
Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S. (2020). A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems, 22(2), 712-733. https://doi.org/10.1109/TITS.2019.2962338
Kwak, B., Park, Y., & Kim, D. (2000). Design of observer for vehicle stability control system. https://www.sae.org/gsdownload/?prodCd=2000-05-0230
Lee, C., Hedrick, K., & Yi, K. (2004). Real-time slip-based estimation of maximum tire-road friction coefficient. IEEE/ASME Transactions on Mechatronics, 9(2), 454-458. https://doi.org/10.1109/TMECH.2004.828622
Lee, G. H., Kim, D.-H., Pak, J. M., & Ahn, C. K. (2024). Vehicle Sideslip Angle Estimation Using Finite Memory Estimation and Dynamics/Kinematics Model Fusion Based on Neural Networks. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2024.3500794
Lee, S., Nakano, K., & Ohori, M. (2015). On-board identification of tyre cornering stiffness using dual Kalman filter and GPS. Vehicle system dynamics, 53(4), 437-448. https://doi.org/10.1080/00423114.2014.999800
Li, B., Quan, Z., Bei, S., Zhang, L., & Mao, H. (2021). An estimation algorithm for tire wear using intelligent tire concept. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(10-11), 2712-2725. https://doi.org/10.1177/0954407021999483
Li, J., & Zhang, J. (2016). Vehicle sideslip angle estimation based on hybrid Kalman filter. Mathematical Problems in Engineering, 2016(1), 3269142. https://doi.org/10.1155/2016/3269142
Li, X., Song, X., & Chan, C. (2014). Reliable vehicle sideslip angle fusion estimation using low-cost sensors. Measurement, 51, 241-258. https://doi.org/10.1016/j.measurement.2014.02.007
Liang, B.-R., & Lin, W.-S. (2012). A new slip ratio observer and its application in electric vehicle wheel slip control. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 41-46). IEEE. https://doi.org/10.1109/ICSMC.2012.6377674
Liao, Y.-W., & Borrelli, F. (2019). An adaptive approach to real-time estimation of vehicle sideslip, road bank angles, and sensor bias. IEEE Transactions on Vehicular Technology, 68(8), 7443-7454. https://doi.org/10.1109/TVT.2019.2919129
Lin, N., Zong, C., & Shi, S. (2018). The method of mass estimation considering system error in vehicle longitudinal dynamics. Energies, 12(1), 52. https://doi.org/10.3390/en12010052
Ling, J., Chen, H., & Xu, F. (2014). Estimation of vehicle sideslip angle with adaptation to road bank angle and roll angle. In Proceedings of SAE-China Congress 2014: Selected Papers (pp. 403-410). Springer. https://doi.org/10.1007/978-3-662-45043-7_41
Lingman, P., & Schmidtbauer, B. (2002). Road slope and vehicle mass estimation using Kalman filtering. Vehicle system dynamics, 37(sup1), 12-23. https://doi.org/10.1080/00423114.2002.11666217
Liu, W., He, H., & Sun, F. (2016). Vehicle state estimation based on minimum model error criterion combining with extended Kalman filter. Journal of the Franklin Institute, 353(4), 834-856. https://doi.org/10.1016/j.jfranklin.2016.01.005
Liu, W., Wang, R., Ding, R., Meng, X., & Yang, L. (2020). On-line estimation of road profile in semi-active suspension based on unsprung mass acceleration. Mechanical Systems and Signal Processing, 135, 106370. https://doi.org/10.1016/j.ymssp.2019.106370
Liu, Y.-J., Dou, C.-H., Shen, F., & Sun, Q.-Y. (2021). Vehicle state estimation based on unscented Kalman filtering and a genetic-particle swarm algorithm. Journal of The Institution of Engineers (India): Series C, 102(2), 447-469. https://doi.org/10.1007/s40032-021-00663-1
Liu, Y., & Cui, D. (2022). Estimation algorithm for vehicle state estimation using ant lion optimization algorithm. Advances in Mechanical Engineering, 14(3), 16878132221085839. https://doi.org/10.1177/16878132221085839
Longobardi, A., Balaga, S., & Gorine, M. E. A. (2025). Tire Thermal Model with State Observer Integration for Enhanced Real-Time Temperature Prediction (0148-7191. https://doi.org/10.4271/2025-01-8756).
M'sirdi, N., Rabhi, A., Fridman, L., Davila, J., & Delanne, Y. (2006). Second order sliding mode observer for estimation of velocities, wheel sleep, radius and stiffness. In 2006 American Control Conference (pp. 6 pp.). IEEE. https://doi.org/10.1109/ACC.2006.1657230
Ma, B., Liu, Y., Gao, Y., Yang, Y., Ji, X., & Bo, Y. (2018). Estimation of vehicle sideslip angle based on steering torque. The International Journal of Advanced Manufacturing Technology, 94(9), 3229-3237. https://doi.org/10.1007/s00170-016-9426-2
Marotta, R., Strano, S., Terzo, M., & Tordela, C. (2024). Multi-output physically analyzed neural network for the prediction of tire–road interaction forces. SAE International Journal of Vehicle Dynamics, Stability, and NVH, 8(10-08-02-0016), 285-308. https://doi.org/10.4271/10-08-02-0016
Mendoza-Petit, M. F., Garcia-Pozuelo, D., Diaz, V., & Olatunbosun, O. (2019). A strain-based method to estimate tire parameters for intelligent tires under complex maneuvering operations. Sensors, 19(13), 2973. https://doi.org/10.3390/s19132973
Miller, S. L., Youngberg, B., Millie, A., Schweizer, P., & Gerdes, J. C. (2001). Calculating longitudinal wheel slip and tire parameters using GPS velocity. In Proceedings of the 2001 American control conference.(Cat. No. 01CH37148) (Vol. 3, pp. 1800-1805). IEEE. https://doi.org/10.1109/ACC.2001.945995
Moaveni, B., Khosravi Roqaye Abad, M., & Nasiri, S. (2015). Vehicle longitudinal velocity estimation during the braking process using unknown input Kalman filter. Vehicle system dynamics, 53(10), 1373-1392. https://doi.org/10.1080/00423114.2015.1038279
Na, J., Chen, A. S., Herrmann, G., Burke, R., & Brace, C. (2017). Vehicle engine torque estimation via unknown input observer and adaptive parameter estimation. IEEE Transactions on Vehicular Technology, 67(1), 409-422. https://doi.org/10.1109/TVT.2017.2737440
Ndjeng, A. N., Lambert, A., Gruyer, D., & Glaser, S. (2009). Experimental comparison of kalman filters for vehicle localization. In 2009 IEEE Intelligent Vehicles Symposium (pp. 441-446). IEEE. https://doi.org/10.1109/IVS.2009.5164318
Nguyen, T., Lechner, B., & Wong, Y. D. (2019). Response-based methods to measure road surface irregularity: A state-of-the-art review. European Transport Research Review, 11(1), 43. https://doi.org/10.1186/s12544-019-0380-6
Nidamanuri, J., Nibhanupudi, C., Assfalg, R., & Venkataraman, H. (2021). A progressive review: Emerging technologies for ADAS driven solutions. IEEE Transactions on Intelligent Vehicles, 7(2), 326-341. https://doi.org/10.1109/TIV.2021.3122898
Nodeh, T. F., Mirzaei, M., & Khosrowjerdi, M. J. (2021). Simultaneous output selection and observer design for vehicle suspension system with unknown road profile. IEEE Transactions on Vehicular Technology, 70(5), 4203-4211. https://doi.org/10.1109/TVT.2021.3068647
Oh, J., & Choi, S. B. (2011). Design of a new composite observer for vehicle velocity and attitude estimation. In IASTED Int. Conf on Control and Applications, Vancouver, BC, Canada (Vol. 102109). https://doi.org/10.2316/P.2011.729-019
Ouahi, M., Stéphant, J., & Meizel, D. (2013). Simultaneous observation of the wheels’ torques and the vehicle dynamic state. Vehicle system dynamics, 51(5), 737-766. https://doi.org/10.1080/00423114.2013.768770
Pan, Y., Nie, X., Li, Z., & Gu, S. (2021). Data-driven vehicle modeling of longitudinal dynamics based on a multibody model and deep neural networks. Measurement, 180, 109541. https://doi.org/10.1016/j.measurement.2021.109541
Pandharipande, A., Cheng, C.-H., Dauwels, J., Gurbuz, S. Z., Ibanez-Guzman, J., Li, G., Piazzoni, A., Wang, P., & Santra, A. (2023). Sensing and machine learning for automotive perception: A review. IEEE Sensors Journal, 23(11), 11097-11115. https://doi.org/10.1109/JSEN.2023.3262134
Panzani, G., Corno, M., & Savaresi, S. (2012). Longitudinal velocity estimation in single-track vehicles. IFAC Proceedings Volumes, 45(16), 1701-1706. https://doi.org/10.3182/20120711-3-BE-2027.00164
Park, G. (2024). Optimal vehicle position estimation using adaptive unscented Kalman filter based on sensor fusion. Mechatronics, 99, 103144. https://doi.org/10.1016/j.mechatronics.2024.103144
Parra, A., Rodriguez, A. J., Zubizarreta, A., & Perez, J. (2020). Validation of a real-time capable multibody vehicle dynamics formulation for automotive testing frameworks based on simulation. IEEE Access, 8, 213253-213265. https://doi.org/10.1109/ACCESS.2020.3040232
Pham, T. P., Sename, O., & Dugard, L. (2021). A nonlinear parameter varying observer for real‐time damper force estimation of an automotive electro‐rheological suspension system. International Journal of Robust and Nonlinear Control, 31(17), 8183-8205. https://doi.org/10.1002/rnc.5583
Piyabongkarn, D., Rajamani, R., Grogg, J. A., & Lew, J. Y. (2008). Development and experimental evaluation of a slip angle estimator for vehicle stability control. IEEE transactions on control systems technology, 17(1), 78-88. https://doi.org/10.1109/TCST.2008.922503
Pomoni, M. (2022). Exploring smart tires as a tool to assist safe driving and monitor tire–road friction. Vehicles, 4(3), 744-765. https://doi.org/10.3390/vehicles4030042
Prakash, R., & Dheer, D. K. (2023). Vehicle state estimation using a maximum likelihood based robust adaptive extended Kalman filter considering unknown white Gaussian process and measurement noise signal. Engineering Research Express, 5(2), 025066. https://doi.org/10.1088/2631-8695/acd73e
Qin, Y., Langari, R., Wang, Z., Xiang, C., & Dong, M. (2017). Road profile estimation for semi-active suspension using an adaptive Kalman filter and an adaptive super-twisting observer. In 2017 American Control Conference (ACC) (pp. 973-978). IEEE. https://doi.org/10.23919/ACC.2017.7963079
Rajamani, R., Phanomchoeng, G., Piyabongkarn, D., & Lew, J. Y. (2011). Algorithms for real-time estimation of individual wheel tire-road friction coefficients. IEEE/ASME Transactions on Mechatronics, 17(6), 1183-1195. https://doi.org/10.1109/TMECH.2011.2159240
Rajamani, R., Piyabongkarn, D., Tsourapas, V., & Lew, J. Y. (2011). Parameter and state estimation in vehicle roll dynamics. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1558-1567. https://doi.org/10.1109/TITS.2011.2164246
Rajendran, S., Spurgeon, S. K., Tsampardoukas, G., & Hampson, R. (2019). Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control. International Journal of Robust and Nonlinear Control, 29(3), 736-765. https://doi.org/10.1002/rnc.4366
Ray, L. R. (1997). Nonlinear tire force estimation and road friction identification: Simulation and experiments. Automatica, 33(10), 1819-1833. https://doi.org/10.1016/S0005-1098(97)00093-9
Reif, K., Renner, K., & Saeger, M. (2008). Using the unscented kalman filter and a non-linear two-track model for vehicle state estimation. IFAC Proceedings Volumes, 41(2), 8570-8575. https://doi.org/10.3182/20080706-5-KR-1001.01449
Reina, G., & Messina, A. (2019). Vehicle dynamics estimation via augmented extended Kalman filtering. Measurement, 133, 383-395. https://doi.org/10.1016/j.measurement.2018.10.030
Reina, G., Paiano, M., & Blanco-Claraco, J.-L. (2017). Vehicle parameter estimation using a model-based estimator. Mechanical Systems and Signal Processing, 87, 227-241. https://doi.org/10.1016/j.ymssp.2016.06.038
Ren, H., Chen, S., Liu, G., & Zheng, K. (2014). Vehicle state information estimation with the unscented Kalman filter. Advances in Mechanical Engineering, 6, 589397. https://doi.org/10.1155/2014/589397
Rezaeian, A., Khajepour, A., Melek, W., Chen, S.-K., & Moshchuk, N. (2016). Simultaneous vehicle real-time longitudinal and lateral velocity estimation. IEEE Transactions on Vehicular Technology, 66(3), 1950-1962. https://doi.org/10.1109/TVT.2016.2580700
Rezaeian, A., Zarringhalam, R., Fallah, S., Melek, W., Khajepour, A., Chen, S.-K., Moshchuck, N., & Litkouhi, B. (2014). Novel tire force estimation strategy for real-time implementation on vehicle applications. IEEE Transactions on Vehicular Technology, 64(6), 2231-2241. https://doi.org/10.1109/TVT.2014.2345695
Rodríguez, A. J., Sanjurjo, E., Pastorino, R., & Naya, M. Á. (2021). State, parameter and input observers based on multibody models and Kalman filters for vehicle dynamics. Mechanical Systems and Signal Processing, 155, 107544. https://doi.org/10.1016/j.ymssp.2020.107544
Ryu, J., & Gerdes, J. C. (2004). Estimation of vehicle roll and road bank angle. In Proceedings of the 2004 American control conference (Vol. 3, pp. 2110-2115). IEEE. https://doi.org/10.23919/ACC.2004.1383772
Ryu, J., Nardi, F., & Moshchuk, N. (2013). Vehicle sideslip angle estimation and experimental validation. In ASME International Mechanical Engineering Congress and Exposition (Vol. 56246, pp. V04AT04A052). American Society of Mechanical Engineers. https://doi.org/10.1115/IMECE2013-64466
Sanjurjo, E. (2016). State observers based on detailed multibody models applied to an automobile. http://hdl.handle.net/2183/17963
Sanjurjo, E., Naya, M. Á., Blanco-Claraco, J. L., Torres-Moreno, J. L., & Giménez-Fernández, A. (2017). Accuracy and efficiency comparison of various nonlinear Kalman filters applied to multibody models. Nonlinear Dynamics, 88(3), 1935-1951. https://doi.org/10.1007/s11071-017-3354-z
Savaresi, S. M., & Tanelli, M. (2010). Active braking control systems design for vehicles. Springer Science & Business Media. https://books.google.com/books?hl=en&lr=&id=MpVrZOWAyf4C&oi=fnd&pg=PR9&dq=Active+braking+control+systems+design+for+vehicle&ots=emCu4yFxfK&sig=k7uxHWgWb_ErYlwHThTUBPx6egY
Selmanaj, D., Corno, M., Panzani, G., & Savaresi, S. M. (2017a). Robust vehicle sideslip estimation based on kinematic considerations. IFAC-PapersOnLine, 50(1), 14855-14860. https://doi.org/10.1016/j.ifacol.2017.08.2513
Selmanaj, D., Corno, M., Panzani, G., & Savaresi, S. M. (2017b). Vehicle sideslip estimation: A kinematic based approach. Control Engineering Practice, 67, 1-12. https://doi.org/10.1016/j.conengprac.2017.06.013
Singh, K. B., Arat, M. A., & Taheri, S. (2019). Literature review and fundamental approaches for vehicle and tire state estimation. Vehicle system dynamics. https://doi.org/10.1080/00423114.2018.1544373
Stephant, J., Charara, A., & Meizel, D. (2004). Linear observers for vehicle sideslip angle: experimental validation. In 2004 IEEE International Symposium on Industrial Electronics (Vol. 1, pp. 341-346). IEEE. https://doi.org/10.1109/ISIE.2004.1571831
Suzuki, T., & Fujimoto, H. (2010). Slip ratio estimation and regenerative brake control without detection of vehicle velocity and acceleration for electric vehicle at urgent brake-turning. In 2010 11th IEEE International Workshop on Advanced Motion Control (AMC) (pp. 273-278). IEEE. https://doi.org/10.1109/AMC.2010.5464122
Tan, C., Cai, Y., Wang, H., Sun, X., & Chen, L. (2023). Vehicle state estimation combining physics-informed neural network and unscented Kalman filtering on manifolds. Sensors, 23(15), 6665. https://doi.org/10.3390/s23156665
Tanelli, M., Ferrara, A., & Giani, P. (2012). Combined vehicle velocity and tire-road friction estimation via sliding mode observers. In 2012 IEEE International Conference on Control Applications (pp. 130-135). IEEE. https://doi.org/10.1109/CCA.2012.6402454
Tanelli, M., Savaresi, S. M., & Cantoni, C. (2006). Longitudinal vehicle speed estimation for traction and braking control systems. In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control (pp. 2790-2795). IEEE. https://doi.org/10.1109/CACSD-CCA-ISIC.2006.4777080
Tao, L., Watanabe, Y., Yamada, S., & Takada, H. (2021). Comparative evaluation of Kalman filters and motion models in vehicular state estimation and path prediction. The Journal of Navigation, 74(5), 1142-1160. https://doi.org/10.1017/S0373463321000370
Teng, S., Hu, X., Deng, P., Li, B., Li, Y., Ai, Y., Yang, D., Li, L., Xuanyuan, Z., & Zhu, F. (2023). Motion planning for autonomous driving: The state of the art and future perspectives. IEEE Transactions on Intelligent Vehicles, 8(6), 3692-3711. https://doi.org/10.1109/TIV.2023.3274536
Törő, O., Bécsi, T., Aradi, S., & Gáspár, P. (2019). Sensitivity and Performance Evaluation of Multiple‐Model State Estimation Algorithms for Autonomous Vehicle Functions. Journal of Advanced Transportation, 2019(1), 7496017. https://doi.org/10.1155/2019/7496017
Tseng, H. E. (2001). Dynamic estimation of road bank angle. Vehicle system dynamics, 36(4-5), 307-328. https://doi.org/10.1076/vesd.36.4.307.3547
Tsunashima, H., Murakami, M., & Miyataa, J. (2006). Vehicle and road state estimation using interacting multiple model approach. Vehicle system dynamics, 44(sup1), 750-758. https://doi.org/10.1080/00423110600885772
Tudon-Martinez, J. C., Fergani, S., Sename, O., Morales-Menendez, R., & Dugard, L. (2014). Online road profile estimation in automotive vehicles. In 2014 European Control Conference (ECC) (pp. 2370-2375). IEEE. https://doi.org/10.1109/ECC.2014.6862539
Tufano, F., Lui, D. G., Battistini, S., Brancati, R., Lenzo, B., & Santini, S. (2024). Vehicle Sideslip Angle estimation under critical road conditions via nonlinear Kalman filter-based state-dependent Interacting Multiple Model approach. Control Engineering Practice, 146, 105901. https://doi.org/10.1016/j.conengprac.2024.105901
Vahidi, A., Druzhinina, M., Stefanopoulou, A., & Peng, H. (2003). Simultaneous mass and time-varying grade estimation for heavy-duty vehicles. In Proceedings of the 2003 American Control Conference, 2003. (Vol. 6, pp. 4951-4956). IEEE. https://doi.org/10.1109/ACC.2003.1242508
Vahidi, A., Stefanopoulou, A., & Peng, H. (2005). Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Vehicle system dynamics, 43(1), 31-55. https://doi.org/10.1080/00423110412331290446
Van Dong, N., Thai, P. Q., Duc, P. M., & Thuan, N. V. (2019). Estimation of vehicle dynamics states using Luenberger observer. International Journal of Mechanical Engineering and Robotics Research, 8(3). https://www.ijmerr.com/uploadfile/2019/0418/20190418111202182.pdf
Venhovens, P. J. T., & Naab, K. (1999). Vehicle dynamics estimation using Kalman filters. Vehicle system dynamics, 32(2-3), 171-184. https://doi.org/10.1076/vesd.32.2.171.2088
Vo-Duy, T., & Ta, M. C. (2018). Slip ratio estimation for traction control of electric vehicles. In 2018 IEEE Vehicle Power and Propulsion Conference (VPPC) (pp. 1-6). IEEE. https://doi.org/10.1109/VPPC.2018.8604980
Wang, B., Cheng, Q., Victorino, A. C., & Charara, A. (2012). Nonlinear observers of tire forces and sideslip angle estimation applied to road safety: Simulation and experimental validation. In 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 1333-1338). IEEE. https://doi.org/10.1109/ITSC.2012.6338830
Wang, Y., Kang, F., Wang, T., & Ren, H. (2018). A robust control method for lateral stability control of in‐wheel motored electric vehicle based on sideslip angle observer. Shock and Vibration, 2018(1), 8197941. https://doi.org/10.1155/2018/8197941
Wang, Y., Xu, L., Zhang, F., Dong, H., Liu, Y., & Yin, G. (2021). An adaptive fault-tolerant EKF for vehicle state estimation with partial missing measurements. IEEE/ASME Transactions on Mechatronics, 26(3), 1318-1327. https://doi.org/10.1109/TMECH.2021.3065210
Wilkin, M., Manning, W., Crolla, D., & Levesley, M. (2006). Use of an extended Kalman filter as a robust tyre force estimator. Vehicle system dynamics, 44(sup1), 50-59. https://doi.org/10.1080/00423110600867325
Wu, L., & Tiso, P. (2016). Nonlinear model order reduction for flexible multibody dynamics: a modal derivatives approach. Multibody System Dynamics, 36(4), 405-425. https://doi.org/10.1007/s11044-015-9476-5
Xia, X., Hang, P., Xu, N., Huang, Y., Xiong, L., & Yu, Z. (2021). Advancing estimation accuracy of sideslip angle by fusing vehicle kinematics and dynamics information with fuzzy logic. IEEE Transactions on Vehicular Technology, 70(7), 6577-6590. https://doi.org/10.1109/TVT.2021.3086095
Xia, X., Hashemi, E., Xiong, L., & Khajepour, A. (2022). Autonomous vehicle kinematics and dynamics synthesis for sideslip angle estimation based on consensus Kalman filter. IEEE transactions on control systems technology, 31(1), 179-192. https://doi.org/10.1109/TCST.2022.3174511
Xia, X., Xiong, L., Liu, W., & Yu, Z. (2018). Automated vehicle attitude and lateral velocity estimation using a 6-D IMU aided by vehicle dynamics. In 2018 IEEE Intelligent Vehicles Symposium (IV) (pp. 1563-1569). IEEE. https://doi.org/10.1109/IVS.2018.8500503
Xiong, L., Xia, X., Lu, Y., Liu, W., Gao, L., Song, S., Han, Y., & Yu, Z. (2019). IMU-based automated vehicle slip angle and attitude estimation aided by vehicle dynamics. Sensors, 19(8), 1930. https://doi.org/10.3390/s19081930
Xiong, L., Xia, X., Lu, Y., Liu, W., Gao, L., Song, S., & Yu, Z. (2020). IMU-based automated vehicle body sideslip angle and attitude estimation aided by GNSS using parallel adaptive Kalman filters. IEEE Transactions on Vehicular Technology, 69(10), 10668-10680. https://doi.org/10.1109/TVT.2020.2983738
Xu, N., Askari, H., Huang, Y., Zhou, J., & Khajepour, A. (2020). Tire force estimation in intelligent tires using machine learning. IEEE Transactions on Intelligent Transportation Systems, 23(4), 3565-3574. https://doi.org/10.1109/TITS.2020.3038155
Xu, Y., Ye, J., Xu, Z., Jin, J., Su, R., & Huang, B. (2025). State estimation of distributed drive vehicles based on an improved adaptive extended Kalman filter. Vehicle system dynamics, 1-21. https://doi.org/10.1080/00423114.2025.2531565
Xue, Z., Cheng, S., Li, L., Zhong, Z., & Mu, H. (2022). A robust unscented M-estimation-based filter for vehicle state estimation with unknown input. IEEE Transactions on Vehicular Technology, 71(6), 6119-6130. https://doi.org/10.1109/TVT.2022.3163207
Yang, C., Shi, W., & Chen, W. (2017). Comparison of unscented and extended Kalman filters with application in vehicle navigation. The Journal of Navigation, 70(2), 411-431. https://doi.org/10.1017/S0373463316000655
Yang, J., Chen, W., & Wang, Y. (2014). Estimate lateral tire force based on yaw moment without using tire model. International Scholarly Research Notices, 2014(1), 934181. https://doi.org/10.1155/2014/934181
Yang, S., Chen, Y., Shi, R., Wang, R., Cao, Y., & Lu, J. (2022). A survey of intelligent tires for tire-road interaction recognition toward autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 7(3), 520-532. https://doi.org/10.1109/TIV.2022.3163588
Yang, X., Zhu, J., Pan, Z., Li, B., & Wang, R. (2020). Adaptive estimator for vehicle roll and road bank angles using inertial sensors. In 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (pp. 1-6). IEEE. https://doi.org/10.1109/CVCI51460.2020.9338602
Ye, Y., Huang, P., Sun, Y., & Shi, D. (2021). MBSNet: A deep learning model for multibody dynamics simulation and its application to a vehicle-track system. Mechanical Systems and Signal Processing, 157, 107716. https://doi.org/10.1016/j.ymssp.2021.107716
You, S.-H., Hahn, J.-O., & Lee, H. (2009). New adaptive approaches to real-time estimation of vehicle sideslip angle. Control Engineering Practice, 17(12), 1367-1379. https://doi.org/10.1016/j.conengprac.2009.07.002
Yu, W., Zhang, X., Guo, K., Karimi, H. R., Ma, F., & Zheng, F. (2013). Adaptive Real‐Time Estimation on Road Disturbances Properties Considering Load Variation via Vehicle Vertical Dynamics. Mathematical Problems in Engineering, 2013(1), 283528. https://doi.org/10.1155/2013/283528
Yu, Z., Hou, X., Leng, B., & Huang, Y. (2022). Mass estimation method for intelligent vehicles based on fusion of machine learning and vehicle dynamic model. Autonomous Intelligent Systems, 2(1), 4. https://doi.org/10.1007/s43684-022-00020-8
Zarringhalam, R., Rezaeian, A., Fallah, S., Khajepour, A., Melek, W., Chen, S.-K., & Litkouhi, B. (2013). Optimal sensor configuration and fault-tolerant estimation of vehicle states. SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 6(2013-01-0175), 83-92. https://doi.org/10.4271/2013-01-0175
Zeitz, M. (1987). The extended Luenberger observer for nonlinear systems. Systems & Control Letters, 9(2), 149-156. https://doi.org/10.1016/0167-6911(87)90021-1
Zhang, B., Du, H., Lam, J., Zhang, N., & Li, W. (2016). A novel observer design for simultaneous estimation of vehicle steering angle and sideslip angle. IEEE Transactions on Industrial Electronics, 63(7), 4357-4366. https://doi.org/10.1109/TIE.2016.2544244
Zhang, W., Wang, Z., Zou, C., Drugge, L., & Nybacka, M. (2019). Advanced vehicle state monitoring: Evaluating moving horizon estimators and unscented Kalman filter. IEEE Transactions on Vehicular Technology, 68(6), 5430-5442. https://doi.org/10.1109/TVT.2019.2909590
Zhang, Y., Li, M., Zhang, Y., Hu, Z., Sun, Q., & Lu, B. (2022). An enhanced adaptive unscented Kalman filter for vehicle state estimation. IEEE Transactions on Instrumentation and Measurement, 71, 1-12. https://doi.org/10.1109/TIM.2022.3180407
Zhang, Y., Zhao, H., Yuan, L., & Chen, H. (2016). Slip ratio estimation for electric vehicle with in-wheel motors based on EKF without detection of vehicle velocity. In 2016 Chinese Control and Decision Conference (CCDC) (pp. 4427-4432). IEEE. https://doi.org/10.1109/CCDC.2016.7531782
Zhang, Y., Zhao, Z., Lu, T., Yuan, L., Xu, W., & Zhu, J. (2009). A comparative study of Luenberger observer, sliding mode observer and extended Kalman filter for sensorless vector control of induction motor drives. In 2009 IEEE Energy Conversion Congress and Exposition (pp. 2466-2473). IEEE. https://doi.org/10.1109/ECCE.2009.5316508
Zhao, L.-H., Liu, Z.-Y., & Chen, H. (2010). Design of a nonlinear observer for vehicle velocity estimation and experiments. IEEE transactions on control systems technology, 19(3), 664-672. https://doi.org/10.1109/TCST.2010.2043104
Zhao, L., & Liu, Z. (2014). Vehicle Velocity and Roll Angle Estimation with Road and Friction Adaptation for Four‐Wheel Independent Drive Electric Vehicle. Mathematical Problems in Engineering, 2014(1), 801628. https://doi.org/10.1155/2014/801628
Zhao, L., Qin, P., Gao, S., Zhang, S., Vantsevich, V. V., & Liu, Z. (2023). A linear variable parameter observer-based road profile height estimation for suspension nonlinear dynamics improvements. IEEE Transactions on Vehicular Technology, 72(7), 8433-8448. https://doi.org/10.1109/TVT.2023.3247790
Zhao, Y.-Q., Li, H.-Q., Lin, F., Wang, J., & Ji, X.-W. (2017). Estimation of road friction coefficient in different road conditions based on vehicle braking dynamics. Chinese Journal of Mechanical Engineering, 30(4), 982-990. https://doi.org/10.1007/s10033-017-0143-z
Zhu, J., Chang, X., Zhang, X., Su, Y., & Long, X. (2022). A Novel Method for the Reconstruction of Road Profiles from Measured Vehicle Responses Based on the Kalman Filter Method. CMES-Computer Modeling in Engineering & Sciences, 130(3). https://doi.org/10.32604/cmes.2022.019140
Zhu, T., & Zheng, H. (2008). Application of unscented Kalman filter to vehicle state estimation. In 2008 ISECS International Colloquium on Computing, Communication, Control, and Management (Vol. 2, pp. 135-139). IEEE. https://doi.org/10.1109/CCCM.2008.124
Zong, C., Hu, D., & Zheng, H. (2013). Dual extended Kalman filter for combined estimation of vehicle state and road friction. Chinese Journal of Mechanical Engineering, 26(2), 313-324. https://doi.org/10.3901/CJME.2013.02.313
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