State Estimation of Multi-Agent Vehicle-Road Interaction Systems
Description
Up-to-date discussions of the challenges and solutions in multi-agent vehicle-road interaction systems
In State Estimation of Multi-Agent Vehicle-Road Interaction Systems, a team of distinguished researchers introduces a novel conceptual framework that defines a system comprising vehicles and local road segments within a connected vehicle (V2X) environment-referred to as the vehicle neighborhood system. Creative estimation methods for both states and parameters within this system have been proposed and potential applications of these methods have been discussed. The book places particular emphasis on estimating and analyzing the motion states of the ego vehicle and the preceding vehicle, as well as the tire road friction coefficient.
The book covers a wide range of topics in the area of vehicle neighborhood systems, including sensor technologies, data fusion, filtering algorithms, engineering applications, and practical implementations of autonomous driving systems. It also explores common challenges in state and parameter estimation for related nonlinear systems, such as sensor data loss, unknown measurement noise, and model parameter perturbations. Corresponding solutions to these issues are proposed and discussed in detail.
The book also includes:
- A thorough introduction to ego-vehicle state estimation with sensor data loss
- Comprehensive explorations of unknown noise and parameter perturbations in ego-vehicle state estimation
- Practical discussions of tire-road friction coefficient estimation with parameter mismatch and data loss
- Complete treatments of preceding vehicle state estimation
Perfect for engineers and professionals with an interest in vehicle state estimation, State Estimation of Multi-Agent Vehicle-Road Interaction Systems will also benefit academics, scientists, and graduate students in areas like robotics, control systems, and autonomous systems.
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Persons
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.
Content
About the Authors viii
Preface ix
1 Introduction 1
1.1 The Definition of Vehicle-Road Interaction System 1
1.2 The Importance of State Estimation for Vehicle-Road Interaction System 2
1.2.1 Enhancing Safety 9
1.2.2 Improving Driving Efficiency 10
1.2.3 Enhancing Autonomous Decision-making Capabilities 10
1.2.4 Supporting ADAS 10
1.2.5 The Foundation of Future Traffic Systems 10
1.2.6 Enhancing User Experience 11
1.3 State Estimation Problems of Vehicle-Road Interaction System 11
1.4 Overview and Organization of the Book 16
References 17
2 Ego-vehicle State Estimation Considering Sensor Data Loss 19
2.1 Introduction 19
2.2 Related Works 20
2.3 State Estimation Based on EKF 25
2.3.1 Preliminary Knowledge 25
2.3.2 Vehicle Model and Problem Statement 28
2.3.3 Methodology 29
2.3.4 Simulation Tests 33
2.3.4.1 The Test on the Asphalt Road 33
2.3.4.2 The Test on the Ice Road 36
2.4 AFTEKF for Estimating Vehicle State with Data Loss 39
2.4.1 Vehicle Model and Problem Statement 40
2.4.2 Methodology 44
2.4.2.1 The FTEKF 45
2.4.2.2 The AFTEKF Algorithm 48
2.4.3 Simulation and Experiment Tests 49
2.4.3.1 The DLC Test in Simulation Systems 50
2.4.3.2 The CS Test in Simulation Systems 53
2.4.3.3 The Real Vehicle Test on the WAR 57
2.4.3.4 The Real Vehicle Test on the DAR 60
2.5 Summary and Future Research 64
References 64
3 Ego-Vehicle State Estimation with Unknown Noise and Parameter Perturbations 69
3.1 Introduction 69
3.2 Related Works 69
3.3 Fuzzy Adaptive Robust Cubature Kalman Filter for Vehicle State Estimation 73
3.3.1 Vehicle Model and Problem Statement 74
3.3.2 Methodology 77
3.3.2.1 Initialization 81
3.3.2.2 Time Update 82
3.3.2.3 Measurement Update 82
3.3.3 Simulation and Experiment Tests 86
3.3.3.1 Double Lane Change Test on High Friction Coefficient Road 86
3.3.3.2 Double Lane Change Test on Low Friction Coefficient Road 91
3.3.3.3 The Real Vehicle Test on the Dry Asphalt Road 95
3.3.3.4 The Real Vehicle Test on the Wet Asphalt Road 99
3.4 Hybridizing Physical and Data-Driven Methods for Vehicle State 103
3.4.1 Vehicle Model and Problem Statement 104
3.4.2 Methodology 106
3.4.2.1 Initialization 106
3.4.2.2 Time Update 107
3.4.2.3 Measurement Update 107
3.4.3 Simulation and Experiment Tests 113
3.4.3.1 The Double Lane Change Test 114
3.4.3.2 The J-Turn Test 118
3.4.3.3 The Real Vehicle Test on the Dry Asphalt Road 122
3.5 Summary and Future Research 128
References 129
4 State Estimation of the Preceding Vehicle with Data Loss and Parameter Perturbations 135
4.1 Introduction 135
4.2 Related Works 136
4.3 Event-Triggered State Estimation for Connected Vehicles with Data Loss 141
4.3.1 Vehicle Model and Problem Statement 143
4.3.2 Methodology 145
4.3.3 Simulation and Experiment Tests 153
4.3.3.1 Simulation Results 153
4.3.3.2 Real Vehicle Test Results 157
4.4 Motion State Estimation of PVs with Unknown Model Parameters 162
4.4.1 Vehicle Model and Problem Statement 165
4.4.2 Methodology 169
4.4.3 Simulation and Experiment Tests 177
4.4.3.1 The Simulation Test 178
4.4.3.2 The Real Vehicle Test 184
4.5 Summary and Future Research 190
References 192
5 Tire-Road Friction Coefficient Estimation with Parameters Mismatch and Data Loss 195
5.1 Introduction 195
5.2 Related Works 197
5.3 TRFC Estimation with Mass Parameter Mismatch Under Complex Driving Scenarios 203
5.3.1 Vehicle Model and Problem Statement 204
5.3.2 Methodology 207
5.3.3 Experiment Tests 214
5.3.3.1 The Test on the Dry Asphalt Road 215
5.3.3.2 The Test on the Ice-Snow Road 220
5.4 A Fault-Tolerant Scheme for Multi-model Ensemble Estimation of Tire-Road Friction Coefficient with Missing Measurements 226
5.4.1 Vehicle Model and Problem Statement 229
5.4.2 Methodology 231
5.4.2.1 TRFC Estimation 236
5.4.2.2 Event-Driven Multi-model Fusion Method 236
5.4.3 Simulation and Experiment Tests 239
5.4.3.1 The Simulation Test 241
5.4.3.2 The Hardware-in-the-Loop Test 252
5.5 Fundamental Estimation for Tire-Road Friction Coefficient: A Model-Based Learning Framework 262
5.5.1 Vehicle Model and Problem Statement 263
5.5.2 Methodology 267
5.5.2.1 Event-Triggering Scheduler 269
5.5.2.2 TRFC Estimation 271
5.5.3 Simulation Tests 278
5.5.3.1 The Effectiveness of ETCKF 279
5.5.3.2 The TRFC Estimation Using the DDEV 283
5.5.3.3 The TRFC Estimation Using the FV 292
5.6 Summary and Future Research 294
References 296
6 Conclusions and Recommendations 301
Index 305
