Autonomous Road Vehicle Path Planning and Tracking Control

Levent Güvenç, PhD, is Professor in the Department of Mechanical and Aerospace Engineering and the Department of Electrical and Computer Engineering at Ohio State University, USA.Bilin Aksun-Güvenç, PhD, is Professor in the Department of Mechanical and Aerospace Engineering at Ohio State University, USA. Sheng Zhu is a Software Engineer on planning and control at DeepRoute.ai with a PhD from the Department of Mechanical and Aerospace Engineering at Ohio State University, USA. Şükrü Yaren Gelbal is Graduate Research Associate in the Department of Electrical and Computer Engineering at Ohio State University, USA.

• Author biographiesPrefaceAbbreviationsChapter 1. Introduction 11.1 Motivation and Introduction 11.2 History of Automated Driving 41.3 ADAS to Autonomous Driving 131.4 Autonomous Driving Architectures 141.5 Cybersecurity Considerations 151.6 Organization and Scope of the Book 161.7 Chapter Summary and Concluding Remarks 16References 16Chapter 2. Vehicle, Path and Path Tracking Models 212.1 Tire Force Model 212.1.1 Introduction 212.1.2 Tire forces/moments and slip 222.1.3 Longitudinal tire force modeling 252.1.4 Lateral tire force modeling 282.1.5 Self-aligning moment model 302.1.6 Coupling of tire forces 322.2 Vehicle longitudinal dynamics model 372.3 Vehicle Lateral Dynamics Model 412.3.1 Geometry of cornering 412.3.2 Single track lateral vehicle model 432.3.3 Augmented single track lateral vehicle model 472.3.4 Linearized single track lateral vehicle model 482.4 Path Model 522.5 Pure Pursuit: Geometry Based Low Speed Path Tracking 582.6 Stanley Method for Path Tracking 592.7 Path Tracking in Reverse Driving and Parking 622.8 Chapter Summary and Concluding Remarks 63References 63Chapter 3. Simulation, Experimentation and Estimation Overview 653.1 Introduction to the Simulation Based Development and Evaluation Process 653.2 Model-in-the-Loop Simulation 683.2.1 Linear and Nonlinear Vehicle Simulation Models 683.2.2 Higher Fidelity Vehicle Simulation Models 693.3 Virtual Environments Used in Simulation 713.3.1 Road Network Creation 713.3.2 Driving Environment Construction 733.3.3 Capabilities 773.4 Hardware-in-the-Loop Simulation 823.5 Experimental Vehicle Testbeds 843.5.1 Unified Approach 843.5.2 Unified AV Functions and Sensors Library 873.6 Estimation 883.6.1 Estimation of the Effective Tire Radius 883.6.2 Slip Slope Method for Road Friction Coefficient Estimation 893.6.3 Results and Discussion 923.7 Chapter Summary and Concluding Remarks 97References 97Chapter 4. Path Description and Generation 1004.1 Introduction 1004.2 Discrete Waypoint Representation 1004.3 Parametric Path Description 1034.3.1 Clothoids 1044.3.2 Bezier Curves 1074.3.3 Polynomial Spline Description 1084.4 Tracking Error Calculation 1134.5 Conclusions 114References 115Chapter 5. Collision Free Path Planning 1175.1 Introduction 1175.2 Elastic Band Method 1215.2.1 Path Structure 1215.2.2 Calculation of Forces 1215.2.3 Reaching Equilibrium Point 1245.2.4 Selected Scenarios 1255.2.5 Results 1275.3 Path Planning with Minimum Curvature Variation 1355.3.1 Optimization based on G2-quintic Splines Path Description 1355.3.2 Reduction of Computation Cost using Lookup Tables 1385.3.3 Geometry-based Collision-free Target Points Generation 1425.3.4 Simulation Results 1455.4 Model-based Trajectory Planning 1485.4.1 Problem Formulation 1485.4.2 Parameterized Vehicle Control 1495.4.3 Constrained Optimization on Curvature Control 1505.4.4 Sampling of the Longitudinal Movements 1555.4.5 Trajectory Evaluation and Selection 1575.4.6 Integration of Road Friction Coefficient Estimation for Safety Enhancement 1595.4.7 Simulation Results in Complex Scenarios 1625.5 Chapter Summary and Concluding Remarks 169References 170Chapter 6. Path Tracking Model Regulation 1746.1 Introduction 1746.2 DOB Design and Frequency Response Analysis 1756.2.1 DOB Derivation and Loop Structure 1756.2.2 Application Examples 1786.2.3 Disturbance Rejection Comparison 1886.3 Q Filter Design 1886.4 Time Delay Performance 1896.5 Chapter Summary and Concluding Remarks 193References 193Chapter 7. Robust Path Tracking Control 1957.1 Model Predictive Control for Path Following 1967.1.1 Formulation of linear adaptive MPC problem 1967.1.2 Estimation of Lateral Velocity 1987.1.3 Experimental Results 2017.2 Design Methodology for Robust Gain-scheduling Law 2047.2.1 Problem Formulation 2047.2.2 Design via Optimization in Linear Matrix Inequalities form 2057.2.3 Parameter-space Gain-scheduling Methodology 2077.3 Robust Gain-scheduling Application to Path Tracking Control 2137.3.1 Car Steering Model and Parameter Uncertainty 2137.3.2 Controller Structure and Design Parameters 2157.3.3 Application of Parameter-space Gain-scheduling 2177.3.4 Comparative Study of LMI Design 2227.3.5 Experimental Results and Discussions 2237.4 Add-on Vehicle Stability Control for Autonomous Driving 2277.4.1 Direct Yaw Moment Control Strategies 2287.4.2 Direct Yaw Moment Distribution via Differential Braking 2347.4.3 Simulation Results and Discussion 2357.5 Chapter Summary and Concluding Remarks 238References 238Chapter 8. Summary and Conclusions 2428.1 Summary 2428.2 Conclusions 244