Human-Like Decision Making and Control for Autonomous Driving
Human-Like Decision Making and Control for Autonomous Driving by Peng Hang, published by Taylor & Francis Group on July 25, 2022, is a comprehensive exploration of advanced research in human-like driving technology. This 186-page book delves into the integration of game theory within a hybrid driving environment, focusing on the design of algorithms for decision-making and control in autonomous vehicles. It begins with a review of existing research and introduces a new theory that enhances the interaction between human drivers and autonomous systems.
Readers will find detailed discussions on system models related to decision-making, driving safety, riding comfort, and travel efficiency. The application of game theory throughout the text facilitates the modeling of both human-like decision-making and collaborative decision-making among connected autonomous vehicles. This framework aims to improve safety and efficiency in complex traffic situations, making the book relevant for students and professionals in automotive engineering, computer engineering, and control engineering.
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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.
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