Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

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The Critique Of Pure Reason

In this paper, we investigate the vehicle lateral dynamics stabilisation problem to enhance vehicle handling by considering time-varying longitudinal velocity. The longitudinal velocity is described by a polytope with finite vertices and a novel technique is proposed to reduce the number of vertices. Since the tyre dynamics is nonlinear, the cornering stiffness is represented via the norm-bounded uncertainty. Concerning the time-varying velocity and the nonlinear tyre model, a linear parameter-varying vehicle model is obtained. As the velocity and the states are measurable, a gain-scheduling state-feedback controller is introduced. In the lateral control, the sideslip angle is required to be as small as possible and the yaw rate is constrained to a certain level. Thus, the control objective is to minimise the sideslip angle while the yaw rate is under a prescribed level or constrain both the sideslip angle and the yaw rate to prescribed levels. To consider the transient response of the closed-loop system,...

Robust gain-scheduling energy-to-peak control of vehicle lateral dynamics stabilisation

Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

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Artificial Intelligence for Vehicle-to-Everything: A Survey

Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

This paper presents a robust fuzzy $H_{\infty }$ control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering. Since vehicle lateral dynamics possesses inherent nonlinearities, the main objective is dedicated to deal with the nonlinear challenge in vehicle lateral dynamics by applying Takagi-Sugeno (T-S) fuzzy modeling approach. First, the nonlinear Brush tire dynamics and the nonlinear functions of longitudinal velocity are represented via a T-S fuzzy modeling technique, and vehicle parametric uncertainties are handled by the norm-bounded uncertainties. An uncertain nonlinear vehicle lateral dynamic T-S fuzzy model is then obtained with multi-fuzzy-rules. The resulting robust fuzzy $H_{\infty }$ state-feedback controller is designed with the parallel-distributed compensation strategy and premise variables, and solved via a set of linear matrix inequalities derived from Lyapunov asymptotic stability and quadratic $H_{\infty }$ performance. Simulations for two different maneuvers are implemented with a high-fidelity, CarSimⓇ, full-vehicle model to verify the effectiveness of the developed approach. It is confirmed from the results that the proposed controller can effectively preserve vehicle lateral stability and enhance yaw handling performance.

Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust Takagi-Sugeno Fuzzy Control

A survey of powertrain configuration studies on hybrid electric vehicles

Gain-scheduled robust control for lateral stability of four-wheel-independent-drive electric vehicles via linear parameter-varying technique

Vehicle control systems require certain vehicle information (e.g., tire–road forces and vehicle sideslip angle) concerning vehicle-dynamic parameters and vehicle–road interaction, which is difficult to measure directly for both technical and economedic reasons. This paper proposes a novel method to estimate lateral tire–road forces and vehicle sideslip angle by utilizing real-time measurements. The estimation method is based on an interacting multiple model (IMM) filter that integrates in-vehicle sensors of in-wheel-motor-driven electric vehicles to adapt multiple vehicle–road system models to variable driving conditions. Based on a four-wheel nonlinear vehicle dynamics model (NVDM) considering extended roll dynamics and load transfer, the vehicle–road system model set of the IMM filter is consists of a linear tire model based NVDM and a nonlinear Dugoff tire model based NVDM. Therefore, the IMM filter can integrate the estimates from two kinds of different vehicle–road system models to improve estimation accuracy. To address system nonlinearities and un-modeled dynamics, the interacting multiple model-unscented Kalman filter (IMM-UKF) and the interacting multiple model-extended Kalman filter (IMM-EKF) are investigated and compared simultaneously. Simulation using Matlab/Simulink-Carsim is carried out to verify the effectiveness of the proposed estimation methods. The results show that the developed estimation methods can accurately estimate lateral tire–road forces and the vehicle sideslip angle.

Estimation of lateral tire–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach

A mu-synthesis robust controller of a four-wheel steering (4WS) vehicle is designed with the optimized weighting functions to attenuate the external disturbances while the yaw rate is chosen as the only feedback signal. Numerical simulation shows that the 4WS vehicle equipped with the mu-synthesis robust controller has better maneuverability and a stronger ability to resist disturbances; it is not sensitive to the variations in vehicle parameters. The mu-synthesis controller has an advantage over the Hprop controller; the latter is thought to be relatively conservative for normal ambient disturbances.

Guodong Yin

Papers

Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust Takagi-Sugeno Fuzzy Control

A survey of powertrain configuration studies on hybrid electric vehicles

Gain-scheduled robust control for lateral stability of four-wheel-independent-drive electric vehicles via linear parameter-varying technique

Estimation of lateral tire–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach

Improving Handling Stability Performance of Four-Wheel Steering Vehicle via $\mu$ -Synthesis Robust Control