This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios.
Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios.
The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
Author(s): Peng Hang, Chen Lv, Xinbo Chen
Publisher: CRC Press
Year: 2022
Language: English
Pages: 200
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Authors
1. Introduction
1.1. Overview of Human-Like Autonomous Driving
1.2. Human-Like Decision Making for Autonomous Vehicles
1.2.1. Model-Based Decision Making
1.2.2. Data-Driven Decision Making
1.2.3. Game Theoretic Decision Making
1.3. Motion Prediction, Planning and Control for Autonomous Vehicles
1.3.1. Motion Prediction
1.3.2. Motion Planning
1.3.3. Motion Control
1.4. Framework of Human-Like Autonomous Driving with Game Theoretic Approaches
2. Human-Like Driving Feature Identification and Representation
2.1. Background
2.2. Driving Style Classification and Recognition
2.2.1. Classification of Driving Styles
2.2.2. Recognition Approaches for Driving Style
2.2.3. Characteristic Analysis of Different Driving Styles for Human-Like Driving
2.3. Driving Aggressiveness of Vehicles
2.3.1. Definition of Driving Aggressiveness
2.3.2. Estimation Approaches of Driving Aggressiveness
2.3.3. Aggressiveness Estimation Model for Human-Like Driving
2.4. Conclusion
3. System Modeling for Decision Making and Control of Autonomous Vehicles
3.1. Background
3.2. Vehicle Model for Decision Making and Control
3.2.1. Vehicle Kinematic Model
3.2.1.1. Mass Point Kinematic Model
3.2.1.2. Bicycle Kinematic Model
3.2.2. Vehicle Dynamic Model
3.2.2.1. Nonlinear Vehicle Dynamic Model
3.2.2.2. Linear Single-Track Model
3.3. Driver Model
3.4. Integrated Model for Human-Like Driving
3.5. Conclusion
4. Motion Planning and Tracking Control of Autonomous Vehicles
4.1. Background
4.2. Human-Like Trajectory Planning for AVs on Highways
4.2.1. Artificial Potential Field Model
4.2.1.1. APF Model for Vehicles
4.2.1.2. APF Model for Road
4.2.1.3. Integrated APF Model for Trajectory Planning
4.2.2. Trajectory Planning Considering Different Social Behaviors of Obstacle Vehicles
4.2.3. Trajectory Planning with APF Considering Trajectory Prediction
4.2.4. Simulation and Discussion
4.2.4.1. Testing Case 1
4.2.4.2. Testing Case 2
4.2.4.3. Testing Case 3
4.2.5. Summary
4.3. Path Planning of AVs on Unstructured Roads
4.3.1. Problem Statement
4.3.2. Path Planning for Static Obstacles
4.3.2.1. Pretreatment of Driving Scenario
4.3.2.2. Path Planning with the Visibility Graph Method
4.3.2.3. Path Optimization Using NMPC
4.3.3. Path Planning for Moving Obstacles
4.3.3.1. Trajectory Prediction for Moving Obstacles
4.3.3.2. NMPC for Path Optimization
4.3.4. Simulation and Validation
4.3.4.1. Case Study 1
4.3.4.2. Case Study 2
4.3.4.3. Case Study 3
4.3.5. Summary
4.4. Path Tracking Control of AVs
4.4.1. Linearized and Discretized Model for Path Tracking Control
4.4.2. Integrated Controller Design
4.4.2.1. Control System Framework
4.4.2.2. Handling Stability Improvement
4.4.2.3. Rollover Prevention
4.4.2.4. Path Tracking Performance
4.4.2.5. LTV-MPC for Integrated Control
4.4.2.6. Weighting Matrices for Control Objectives
4.4.3. Simulation and Analysis
4.4.3.1. Double-Lane Change Maneuver
4.4.3.2. Sinusoidal Path Maneuver
4.4.4. Summary
4.5. Conclusion
5. Human-Like Decision Making for Autonomous Vehicles with Noncooperative Game Theoretic Method
5.1. Background
5.2. Human-Like Lane Change for AVs
5.2.1. Problem Description and Human-Like Decision Making Framework
5.2.1.1. Human-Like Lane Change Issue
5.2.1.2. System Framework for Human-Like Decision Making
5.2.2. Human-like Decision Making Based on Noncooperative Game Theory
5.2.2.1. Cost Function for Lane-Change Decision Making
5.2.2.2. Noncooperative Decision Making Based on Nash Equilibrium
5.2.2.3. Noncooperative Decision Making Based on Stackelberg Equilibrium
5.2.3. Testing Results and Performance Evaluation
5.2.3.1. Scenario A
5.2.3.2. Scenario B
5.2.3.3. Discussion of the Testing Results
5.2.4. Summary
5.3. Human-Like Decision Making of AVs at Unsignalized Roundabouts
5.3.1. Problem Formulation and System Framework
5.3.1.1. Decision Making of AVs at Unsignalized Roundabouts
5.3.1.2. Decision Making Framework for AVs
5.3.2. Motion Prediction of AVs for Decision Making
5.3.3. Algorithm Design of Decision Making Using the Game Theoretic Approach
5.3.3.1. Payoff Function of Decision Making Considering One Opponent
5.3.3.2. Payoff Function of Decision Making Considering Multiple Opponents
5.3.3.3. Constraints of Decision Making
5.3.3.4. Decision Making with the Game Theory and MPC Optimization
5.3.4. Testing Results and Analysis
5.3.4.1. Testing Case 1
5.3.4.2. Testing Case 2
5.3.4.3. Testing Case 3
5.3.5. Summary
5.4. Conclusion
6. Decision Making for CAVs with Cooperative Game Theoretic Method
6.1. Background
6.2. Cooperative Lane-Change and Merging of CAVs on Highways
6.2.1. Problem Formulation and System Framework
6.2.1.1. Problem Formulation
6.2.1.2. Cooperative Decision-Making Framework for CAVs
6.2.2. Motion Prediction of CAVs
6.2.3. Decision Making Using the Coalitional Game Approach
6.2.3.1. Formulation of the Coalitional Game for CAVs
6.2.3.2. Cost Function for the Decision Making of an Individual CAV
6.2.3.3. Constraints of the Decision Making
6.2.3.4. Decision Making with the Coalitional Game Approach
6.2.4. Testing, Validation and Discussion
6.2.4.1. Case Study 1
6.2.4.2. Case Study 2
6.2.5. Summary
6.3. Cooperative Decision Making of CAVs at Unsignalized Roundabouts
6.3.1. Decision Making with the Cooperative Game Theory
6.3.2. Testing Results and Analysis
6.3.2.1. Testing Case 1
6.3.2.2. Testing Case 2
6.3.2.3. Testing Case 3
6.3.2.4. Discussion
6.3.3. Summary
6.4. Conclusion
7. Conclusion, Discussion and Prospects
7.1. Human-Like Modeling for AVs
7.2. Human-Like Decision-Making Algorithm
7.3. Cooperative Decision Making Considering Personalized Driving
Bibliography
Index
