Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

[1]  Yoichi Hori,et al.  Lateral Stability Control of In-Wheel-Motor-Driven Electric Vehicles Based on Sideslip Angle Estimation Using Lateral Tire Force Sensors , 2012, IEEE Transactions on Vehicular Technology.

[2]  Guodong Yin,et al.  Estimation of lateral tire-road forces and sideslip angle for electric vehicles using interacting multiple model filter approach , 2015, J. Frankl. Inst..

[3]  Tor Arne Johansen,et al.  Vehicle velocity estimation using nonlinear observers , 2006, Autom..

[4]  Edoardo Sabbioni,et al.  On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results , 2011 .

[5]  Hyeongcheol Lee,et al.  Reliability Indexed Sensor Fusion and Its Application to Vehicle Velocity Estimation , 2006 .

[6]  Ali Charara,et al.  Onboard Real-Time Estimation of Vehicle Lateral Tire–Road Forces and Sideslip Angle , 2011, IEEE/ASME Transactions on Mechatronics.

[7]  Yong Shi,et al.  A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .

[8]  Jihua Huang,et al.  A Low-Order DGPS-Based Vehicle Positioning System Under Urban Environment , 2006, IEEE/ASME Transactions on Mechatronics.

[9]  Junmin Wang,et al.  Sideslip Angle Estimation of an Electric Ground Vehicle via Finite-Frequency H∞ Approach , 2016 .

[10]  Christopher Edwards,et al.  Optimal braking and estimation of tyre friction in automotive vehicles using sliding modes , 2007, Int. J. Syst. Sci..

[11]  María Jesús López Boada,et al.  Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices , 2018, Sensors.

[12]  Changsun Ahn,et al.  Robust estimation of road friction coefficient using lateral and longitudinal vehicle dynamics , 2012 .

[13]  David Cebon,et al.  Parameter and state estimation for articulated heavy vehicles , 2011 .

[14]  M. Nguyen-H,et al.  Improving GPS/INS Integration through Neural Networks , 2010, ArXiv.

[15]  Kanghyun Nam,et al.  Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles , 2015, Sensors.

[16]  Zhiyuan Liu,et al.  Design of a Nonlinear Observer for Vehicle Velocity Estimation and Experiments , 2011, IEEE Transactions on Control Systems Technology.

[17]  Kunsoo Huh,et al.  Monitoring System Design for Lateral Vehicle Motion , 2011, IEEE Transactions on Vehicular Technology.

[18]  Ching-Yao Chan,et al.  Reliable vehicle sideslip angle fusion estimation using low-cost sensors , 2014 .

[19]  Keith J. Burnham,et al.  Dual extended Kalman filter for vehicle state and parameter estimation , 2006 .

[20]  Xiaohai Lin,et al.  Energy-optimal adaptive cruise control combining model predictive control and dynamic programming , 2018 .

[21]  Zhang Wei,et al.  An ABS Control Strategy for Commercial Vehicle , 2015, IEEE/ASME Transactions on Mechatronics.

[22]  Rolf Isermann,et al.  Cornering Stiffness and Sideslip Angle Estimation for Integrated Vehicle Dynamics Control , 2016 .

[23]  Selim Solmaz,et al.  Simultaneous estimation of road friction and sideslip angle based on switched multiple non-linear observers , 2012 .

[24]  Biao Ma,et al.  Estimation of vehicle sideslip angle based on steering torque , 2018 .

[25]  Yugong Luo,et al.  Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion , 2016 .

[26]  Wei Liu,et al.  Vehicle state estimation based on Minimum Model Error criterion combining with Extended Kalman Filter , 2016, J. Frankl. Inst..

[27]  J.-H. Yoon,et al.  Estimation of vehicle sideslip angle and tire-road friction coefficient based on magnetometer with GPS , 2016 .

[28]  Edoardo Sabbioni,et al.  A methodology for vehicle sideslip angle identification: comparison with experimental data , 2007 .

[29]  John A. Grogg,et al.  Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients , 2006, IEEE/ASME Transactions on Mechatronics.

[30]  J C Cadiou,et al.  Non-linear tyre forces estimation based on vehicle dynamics observation in a finite time , 2004 .

[31]  Minglei Shu,et al.  Non-linear velocity observer for vehicles with tyre–road friction estimation , 2018, Int. J. Syst. Sci..

[32]  A. Goodarzi,et al.  Design of a VDC System for All-Wheel Independent Drive Vehicles , 2007, IEEE/ASME Transactions on Mechatronics.

[33]  Qiang Wang,et al.  Improved adaptive lane‐keeping control for four‐wheel steering vehicles without lateral velocity measurements , 2017 .

[34]  Shahram Azadi,et al.  Road profile estimation using neural network algorithm , 2010 .

[35]  H. Tsunashima,et al.  Vehicle and road state estimation using interacting multiple model approach , 2006 .

[36]  Rajesh Rajamani,et al.  Development and Experimental Evaluation of a Slip Angle Estimator for Vehicle Stability Control , 2006, IEEE Transactions on Control Systems Technology.

[37]  Xiao Qi,et al.  Simultaneous modeling of car-following and lane-changing behaviors using deep learning , 2019, Transportation Research Part C: Emerging Technologies.

[38]  David M. Bevly,et al.  Using GPS with a model-based estimator to estimate critical vehicle states , 2010 .

[39]  Tahereh Binazadeh,et al.  Application of neural network and genetic algorithm in identification of a model of a variable mass underwater vehicle , 2015 .

[40]  Denes Fodor,et al.  Tyre–road friction coefficient estimation based on the discrete-time extended Kalman filter , 2015 .

[41]  Hongbin Ren,et al.  A Robust Control Method for Lateral Stability Control of In-Wheel Motored Electric Vehicle Based on Sideslip Angle Observer , 2018 .

[42]  Donald Margolis,et al.  The Controller Output Observer: Estimation of Vehicle Tire Cornering and Normal Forces , 2008 .

[43]  Felipe Espinosa,et al.  Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation , 2010, Sensors.

[44]  Salvatore Strano,et al.  Vehicle sideslip angle estimation via a Riccati equation based nonlinear filter , 2017 .

[45]  Salvatore Strano,et al.  Constrained nonlinear filter for vehicle sideslip angle estimation withno a priori knowledge of tyre characteristics , 2018 .

[46]  Mohammad A. Jaradat,et al.  Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS , 2013 .

[47]  Seungyong Lee,et al.  On-board identification of tyre cornering stiffness using dual Kalman filter and GPS , 2015 .

[48]  Tae-Suk Bae,et al.  Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation , 2019, J. Sensors.

[49]  Jianqiang Wang,et al.  Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies , 2016, IEEE Transactions on Intelligent Transportation Systems.

[50]  Junmin Wang,et al.  Tire–road friction coefficient and tire cornering stiffness estimation based on longitudinal tire force difference generation , 2013 .

[51]  Shahram Azadi,et al.  Estimation of road friction coefficient using extended Kalman filter, recursive least square, and neural network , 2016 .

[52]  Matt C. Best,et al.  A Parameter Identifying a Kalman Filter Observer for Vehicle Handling Dynamics , 2006 .

[53]  Kaspar Althoefer,et al.  Non-linear observer for slip estimation of tracked vehicles , 2008 .

[54]  Youngjin Park,et al.  Sliding-mode-based parameter identification with application to tire pressure and tire-road friction , 2011 .

[55]  Juan Antonio Cabrera Carrillo,et al.  Robust Road Condition Detection System Using In-Vehicle Standard Sensors , 2015, Sensors.

[56]  Guodong Yin,et al.  Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust Takagi-Sugeno Fuzzy Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[57]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[58]  Ching-Yao Chan,et al.  A Reliable Fusion Methodology for Simultaneous Estimation of Vehicle Sideslip and Yaw Angles , 2016, IEEE Transactions on Vehicular Technology.

[59]  Bo-Chiuan Chen,et al.  Sideslip angle estimation using extended Kalman filter , 2008 .

[60]  Haiqing Li,et al.  Estimation of Road Friction Coefficient in Different Road Conditions Based on Vehicle Braking Dynamics , 2017 .

[61]  Aleksander B. Hac,et al.  Estimation of Vehicle Side Slip Angle and Yaw Rate , 2000 .

[62]  María Jesús López Boada,et al.  Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm , 2016 .

[63]  Yantao Tian,et al.  Longitudinal Collision Avoidance Control of Electric Vehicles Based on a New Safety Distance Model and Constrained-Regenerative-Braking-Strength-Continuity Braking Force Distribution Strategy , 2016, IEEE Transactions on Vehicular Technology.

[64]  Juan Yang,et al.  Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network , 2016, J. Sensors.

[65]  Zhicheng Wu,et al.  Effective assessment of tyre–road friction coefficient using a hybrid estimator , 2014 .

[66]  S. Glaser,et al.  Tire-road forces estimation using extended Kalman filter and sideslip angle evaluation , 2008, 2008 American Control Conference.

[67]  Honghai Liu,et al.  Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault , 2012, IEEE Transactions on Fuzzy Systems.

[68]  Jian Song,et al.  A variable structure adaptive extended Kalman filter for vehicle slip angle estimation , 2011 .

[69]  Hui Zhang,et al.  Robust gain-scheduling energy-to-peak control of vehicle lateral dynamics stabilisation , 2014 .

[70]  Myoungho Sunwoo,et al.  Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles , 2013 .

[71]  Bei Shaoyi,et al.  Vehicle Sideslip Angle Estimation Based on General Regression Neural Network , 2016 .

[72]  Guodong Yin,et al.  Cubature kalman filter-based state estimation for distributed drive electric vehicles , 2016, 2016 35th Chinese Control Conference (CCC).

[73]  Selim Solmaz,et al.  A nonlinear sideslip observer design methodology for automotive vehicles based on a rational tire model , 2012 .

[74]  Zhuoping Yu,et al.  Sideslip angle estimation based on input–output linearisation with tire–road friction adaptation , 2010 .

[75]  Guangzhong Dong,et al.  A method for state of energy estimation of lithium-ion batteries based on neural network model , 2015 .

[76]  Qi Cheng,et al.  Nonlinear observers of tire forces and sideslip angle estimation applied to road safety: Simulation and experimental validation , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[77]  Beatriz L. Boada,et al.  Sideslip angle estimator based on ANFIS for vehicle handling and stability , 2015 .

[78]  Hocine Imine,et al.  Heavy duty vehicle tyre forces estimation using variable gain sliding mode observer , 2013 .

[79]  Iraj Davoodabadi,et al.  Identification of tire forces using Dual Unscented Kalman Filter algorithm , 2014 .

[80]  Kiriakos Kiriakidis,et al.  A Comparison of H∞ with Kalman Filtering in Vehicle State and Parameter Identification , 2006 .

[81]  Jyotishman Ghosh,et al.  A Deep Learning based Virtual Sensor for Vehicle Sideslip Angle Estimation: Experimental Results , 2018 .

[82]  Michael Defoort,et al.  Higher-order sliding mode observer for estimation of tyre friction in ground vehicles , 2014 .

[83]  Stratis Kanarachos,et al.  Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares , 2018, Neural Computing and Applications.

[84]  Yantao Tian,et al.  Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information , 2015 .

[85]  Yingfeng Cai,et al.  Robust sideslip angle observer with regional stability constraint for an uncertain singular intelligent vehicle system , 2018, IET Control Theory & Applications.

[86]  J. Christian Gerdes,et al.  Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control , 2004 .

[87]  Christopher Edwards,et al.  Tyre—road friction estimation — a comparative study , 2008 .

[88]  Maya Dawood,et al.  Vehicle geo-localization based on IMM-UKF data fusion using a GPS receiver, a video camera and a 3D city model , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[89]  Changsun Ahn,et al.  Robust estimation of road friction coefficient , 2011, Proceedings of the 2011 American Control Conference.

[90]  Jin Zhang,et al.  Coordinative traction control of vehicles based on identification of the tyre–road friction coefficient , 2016 .

[91]  María Jesús López Boada,et al.  A robust observer based on H∞ filtering with parameter uncertainties combined with Neural Networks for estimation of vehicle roll angle , 2018 .

[92]  Daniel Álvarez Mántaras,et al.  Tyre–road grip coefficient assessment – Part II: online estimation using instrumented vehicle, extended Kalman filter, and neural network , 2013 .

[93]  Tor Arne Johansen,et al.  Nonlinear vehicle side-slip estimation with friction adaptation , 2008, Autom..

[94]  Francesco Borrelli,et al.  Control of Connected and Automated Vehicles: State of the Art and Future Challenges , 2018, Annu. Rev. Control..

[95]  O. Sename,et al.  Vehicle dynamic stability improvements through gain-scheduled steering and braking control , 2011 .

[96]  Nedjeljko Peric,et al.  Neural network based tire/road friction force estimation , 2008, Eng. Appl. Artif. Intell..

[97]  Gae-won You,et al.  Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach , 2016 .

[98]  Seibum B. Choi,et al.  Real-Time Individual Tire Force Estimation for an All-Wheel Drive Vehicle , 2018, IEEE Transactions on Vehicular Technology.

[99]  Takeshi Nishida,et al.  Dynamic state estimation using particle filter and adaptive vector quantizer , 2009, 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA).

[100]  Yoichi Hori,et al.  Lateral Stability Control of Electric Vehicle Based On Disturbance Accommodating Kalman Filter using the Integration of Single Antenna GPS Receiver and Yaw Rate Sensor , 2013 .

[101]  Guodong Yin,et al.  Gain-Scheduled Vehicle Handling Stability Control Via Integration of Active Front Steering and Suspension Systems , 2016 .

[102]  Rui Yu,et al.  Vehicle Handling Evaluation Models Using Artificial Neural Networks , 2015 .

[103]  Paul J.Th. Venhovens,et al.  Vehicle Dynamics Estimation Using Kalman Filters , 1999 .

[104]  Jing Li,et al.  Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter , 2016 .

[105]  Jaeseung Cheon,et al.  Vehicle velocity estimation using effective inertia for an in-wheel electric vehicle , 2014 .

[106]  Changfu Zong,et al.  Dual extended Kalman filter for combined estimation of vehicle state and road friction , 2013 .

[107]  Yingfeng Cai,et al.  Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion , 2018, Sensors.

[108]  Zhuoping Yu,et al.  Estimation of maximum road friction coefficient based on Lyapunov method , 2016 .

[109]  Tiantian Jiang,et al.  Adaptive control of hypersonic vehicles based on characteristic models with fuzzy neural network estimators , 2017 .

[110]  Jianxiao Zou,et al.  Vehicle State Estimation Using Cubature Kalman Filter , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[111]  Zhuoping Yu,et al.  Estimation of the vehicle speed in the driving mode for a hybrid electric car based on an unscented Kalman filter , 2015 .

[112]  María Jesús López Boada,et al.  Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States , 2017, Sensors.

[113]  Ali Charara,et al.  Observers for vehicle tyre/road forces estimation: experimental validation , 2010 .

[114]  Lingling Shi,et al.  A novel control approach for path tracking of a force-controlled two-wheel-steer four-wheel-drive vehicle , 2019 .

[115]  Jian Song,et al.  UKF-based adaptive variable structure observer for vehicle sideslip with dynamic correction , 2016 .

[116]  Kyongsu Yi,et al.  Estimation of Tire Forces for Application to Vehicle Stability Control , 2010, IEEE Transactions on Vehicular Technology.

[117]  Junmin Wang,et al.  Sideslip Angle Estimation of an Electric Ground Vehicle via Finite-Frequency $\mathcal {H}_{\infty}$ Approach , 2016, IEEE Transactions on Transportation Electrification.

[118]  A. Charara,et al.  An Observer of Tire–Road Forces and Friction for Active Security Vehicle Systems , 2007, IEEE/ASME Transactions on Mechatronics.

[119]  Kunsoo Huh,et al.  Estimating the Maximum Road Friction Coefficient with Uncertainty Using Deep Learning , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[120]  Linhui Li,et al.  Traffic Scene Segmentation Based on RGB-D Image and Deep Learning , 2018, IEEE Transactions on Intelligent Transportation Systems.

[121]  J. Stephant,et al.  Observability matrix and parameter identification: application to vehicle tire cornering stiffness , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[122]  Seibum Choi,et al.  Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient , 2013, IEEE Transactions on Vehicular Technology.

[123]  Miroslav B. Milovanović,et al.  Quasi-Sliding Mode Control With Orthogonal Endocrine Neural Network-Based Estimator Applied in Anti-Lock Braking System , 2016, IEEE/ASME Transactions on Mechatronics.

[124]  Hong Chen,et al.  Design of a reduced-order non-linear observer for vehicle velocities estimation , 2013 .

[125]  Jun Zeng,et al.  A novel multi-sensors fusion framework based on Kalman Filter and neural network for AFS application , 2015 .

[126]  Amir Khajepour,et al.  Real-time estimation of the road bank and grade angles with unknown input observers , 2017 .

[127]  Shahram Azadi,et al.  Road profile estimation using wavelet neural network and 7-DOF vehicle dynamic systems , 2012 .

[128]  María Jesús López Boada,et al.  A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation , 2016, Sensors.

[129]  Yugong Luo,et al.  Tire–road friction coefficient estimation based on the resonance frequency of in-wheel motor drive system , 2016 .

[130]  L. Alexander,et al.  Estimation of Tire-Road Friction Coefficient Using a Novel Wireless Piezoelectric Tire Sensor , 2011, IEEE Sensors Journal.

[131]  Francesco Borrelli,et al.  Tyre–road friction coefficient estimation based on tyre sensors and lateral tyre deflection: modelling, simulations and experiments , 2013 .

[132]  Wan-Suk Yoo,et al.  Estimation of Tire–Road Friction Based on Onboard 6-DoF Acceleration Measurement , 2015, IEEE Transactions on Vehicular Technology.

[133]  Rajesh Rajamani,et al.  Nonlinear Observer for Bounded Jacobian Systems, With Applications to Automotive Slip Angle Estimation , 2011, IEEE Transactions on Automatic Control.

[134]  Konghui Guo,et al.  A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces , 2014 .

[135]  Shuzhi Sam Ge,et al.  Sliding-Mode-Observer-Based Adaptive Slip Ratio Control for Electric and Hybrid Vehicles , 2012, IEEE Transactions on Intelligent Transportation Systems.

[136]  Hong Chen,et al.  Estimation of Vehicle Yaw Rate and Side Slip Angle using Moving Horizon Strategy , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[137]  M. Romano,et al.  Development and validation of a Kalman filter-based model for vehicle slip angle estimation , 2014 .

[138]  Antonella Ferrara,et al.  Combined vehicle velocity and tire-road friction estimation via sliding mode observers , 2012, 2012 IEEE International Conference on Control Applications.

[139]  Shuo Cheng,et al.  Fusion Algorithm Design Based on Adaptive SCKF and Integral Correction for Side-Slip Angle Observation , 2018, IEEE Transactions on Industrial Electronics.

[140]  Weihua Li,et al.  Comparative study of vehicle tyre–road friction coefficient estimation with a novel cost-effective method , 2014 .

[141]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[142]  Qijun Chen,et al.  Joint estimation of states and parameters of vehicle model using cubature Kalman filter , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[143]  L. Fridman,et al.  Second order sliding mode observer for estimation of velocities, wheel sleep, radius and stiffness , 2006, 2006 American Control Conference.

[144]  J. Kim.  Effect of vehicle model on the estimation of lateral vehicle dynamics , 2010 .

[145]  Nong Zhang,et al.  Real-time identification of vehicle motion-modes using neural networks , 2015 .

[146]  Yugong Luo,et al.  In–wheel motor electric vehicle state estimation by using unscented particle filter , 2015 .

[147]  Jonathan Brembeck,et al.  Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation , 2019, Sensors.

[148]  Mingming Dong,et al.  Vehicle System State Estimation Based on Adaptive Unscented Kalman Filtering Combing With Road Classification , 2017, IEEE Access.

[149]  Jing Na,et al.  MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation , 2019, IEEE Transactions on Vehicular Technology.

[150]  Qi Cheng,et al.  A new nonlinear observer of sideslip angle with unknown vehicle parameter using the dual unscented Kalman filter , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[151]  Yoichi Hori,et al.  Estimation of Sideslip and Roll Angles of Electric Vehicles Using Lateral Tire Force Sensors Through RLS and Kalman Filter Approaches , 2013, IEEE Transactions on Industrial Electronics.

[152]  Khaled S. Hatamleh,et al.  Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method , 2015, Appl. Soft Comput..

[153]  Joachim Rudolph,et al.  Vehicle state estimation for anti-lock control with nonlinear observer , 2015 .

[154]  Fei Wang,et al.  Implementation of EKF for Vehicle Velocities Estimation on FPGA , 2013, IEEE Transactions on Industrial Electronics.

[155]  Saied Taheri,et al.  Estimation of the tyre–road maximum friction coefficient and slip slope based on a novel tyre model , 2015 .

[156]  Fook Fah Yap,et al.  Identification of Spring-Force Factors of Suspension Systems Using Progressive Neural Network on a Validated Computer Model , 2003 .

[157]  Hamid M. Lankarani,et al.  Hybrid simulation of a dynamic multibody vehicle suspension system using neural network modeling fit of tire data , 2016 .

[158]  Andreas Kugi,et al.  Unscented Kalman filter for vehicle state estimation , 2011 .

[159]  Abdelaziz Benallegue,et al.  Rollover Risk Prediction of Heavy Vehicle Using High-Order Sliding-Mode Observer: Experimental Results , 2014, IEEE Transactions on Vehicular Technology.

[160]  R. Krishna Kumar,et al.  A comparison of various algorithms to extract Magic Formula tyre model coefficients for vehicle dynamics simulations , 2015 .

[161]  J. Kim.  Identification of lateral tyre force dynamics using an extended Kalman filter from experimental road test data , 2009 .

[162]  Hamid Taghavifar,et al.  Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin , 2013, Neural Computing and Applications.

[163]  D. Meizel,et al.  Linear observers for vehicle sideslip angle : experimental validation , 2004, 2004 IEEE International Symposium on Industrial Electronics.

[164]  Mara Tanelli,et al.  Real-time identification of tire-road friction conditions , 2008, 2008 IEEE International Conference on Control Applications.

[165]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[166]  Louman Li,et al.  Vehicle velocity estimation for real-time dynamic stability control , 2009 .

[167]  Lorenzo Fagiano,et al.  A direct Moving Horizon approach to vehicle side-slip angle estimation , 2010, 49th IEEE Conference on Decision and Control (CDC).