Yan Wang, PhD, is currently a Research Fellow at the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University. Guodong Yin, PhD, is a Professor with the School of Mechanical Engineering, Southeast University. His research is focused on vehicle dynamics and control, automated vehicles, and connected vehicles. Chao Huang, PhD, is currently a Senior Lecturer at The University of Adelaide, Australia.

• About the Authors viiiPreface ix1 Introduction 11.1 The Definition of Vehicle–Road Interaction System 11.2 The Importance of State Estimation for Vehicle–Road Interaction System 21.2.1 Enhancing Safety 91.2.2 Improving Driving Efficiency 101.2.3 Enhancing Autonomous Decision-making Capabilities 101.2.4 Supporting ADAS 101.2.5 The Foundation of Future Traffic Systems 101.2.6 Enhancing User Experience 111.3 State Estimation Problems of Vehicle–Road Interaction System 111.4 Overview and Organization of the Book 16References 172 Ego-vehicle State Estimation Considering Sensor Data Loss 192.1 Introduction 192.2 Related Works 202.3 State Estimation Based on EKF 252.3.1 Preliminary Knowledge 252.3.2 Vehicle Model and Problem Statement 282.3.3 Methodology 292.3.4 Simulation Tests 332.3.4.1 The Test on the Asphalt Road 332.3.4.2 The Test on the Ice Road 362.4 AFTEKF for Estimating Vehicle State with Data Loss 392.4.1 Vehicle Model and Problem Statement 402.4.2 Methodology 442.4.2.1 The FTEKF 452.4.2.2 The AFTEKF Algorithm 482.4.3 Simulation and Experiment Tests 492.4.3.1 The DLC Test in Simulation Systems 502.4.3.2 The CS Test in Simulation Systems 532.4.3.3 The Real Vehicle Test on the WAR 572.4.3.4 The Real Vehicle Test on the DAR 602.5 Summary and Future Research 64References 643 Ego-Vehicle State Estimation with Unknown Noise and Parameter Perturbations 693.1 Introduction 693.2 Related Works 693.3 Fuzzy Adaptive Robust Cubature Kalman Filter for Vehicle State Estimation 733.3.1 Vehicle Model and Problem Statement 743.3.2 Methodology 773.3.2.1 Initialization 813.3.2.2 Time Update 823.3.2.3 Measurement Update 823.3.3 Simulation and Experiment Tests 863.3.3.1 Double Lane Change Test on High Friction Coefficient Road 863.3.3.2 Double Lane Change Test on Low Friction Coefficient Road 913.3.3.3 The Real Vehicle Test on the Dry Asphalt Road 953.3.3.4 The Real Vehicle Test on the Wet Asphalt Road 993.4 Hybridizing Physical and Data-Driven Methods for Vehicle State 1033.4.1 Vehicle Model and Problem Statement 1043.4.2 Methodology 1063.4.2.1 Initialization 1063.4.2.2 Time Update 1073.4.2.3 Measurement Update 1073.4.3 Simulation and Experiment Tests 1133.4.3.1 The Double Lane Change Test 1143.4.3.2 The J-Turn Test 1183.4.3.3 The Real Vehicle Test on the Dry Asphalt Road 1223.5 Summary and Future Research 128References 1294 State Estimation of the Preceding Vehicle with Data Loss and Parameter Perturbations 1354.1 Introduction 1354.2 Related Works 1364.3 Event-Triggered State Estimation for Connected Vehicles with Data Loss 1414.3.1 Vehicle Model and Problem Statement 1434.3.2 Methodology 1454.3.3 Simulation and Experiment Tests 1534.3.3.1 Simulation Results 1534.3.3.2 Real Vehicle Test Results 1574.4 Motion State Estimation of PVs with Unknown Model Parameters 1624.4.1 Vehicle Model and Problem Statement 1654.4.2 Methodology 1694.4.3 Simulation and Experiment Tests 1774.4.3.1 The Simulation Test 1784.4.3.2 The Real Vehicle Test 1844.5 Summary and Future Research 190References 1925 Tire–Road Friction Coefficient Estimation with Parameters Mismatch and Data Loss 1955.1 Introduction 1955.2 Related Works 1975.3 TRFC Estimation with Mass Parameter Mismatch Under Complex Driving Scenarios 2035.3.1 Vehicle Model and Problem Statement 2045.3.2 Methodology 2075.3.3 Experiment Tests 2145.3.3.1 The Test on the Dry Asphalt Road 2155.3.3.2 The Test on the Ice-Snow Road 2205.4 A Fault-Tolerant Scheme for Multi-model Ensemble Estimation of Tire–Road Friction Coefficient with Missing Measurements 2265.4.1 Vehicle Model and Problem Statement 2295.4.2 Methodology 2315.4.2.1 TRFC Estimation 2365.4.2.2 Event-Driven Multi-model Fusion Method 2365.4.3 Simulation and Experiment Tests 2395.4.3.1 The Simulation Test 2415.4.3.2 The Hardware-in-the-Loop Test 2525.5 Fundamental Estimation for Tire–Road Friction Coefficient: A Model-Based Learning Framework 2625.5.1 Vehicle Model and Problem Statement 2635.5.2 Methodology 2675.5.2.1 Event-Triggering Scheduler 2695.5.2.2 TRFC Estimation 2715.5.3 Simulation Tests 2785.5.3.1 The Effectiveness of ETCKF 2795.5.3.2 The TRFC Estimation Using the DDEV 2835.5.3.3 The TRFC Estimation Using the FV 2925.6 Summary and Future Research 294References 2966 Conclusions and Recommendations 301Index 305