Decision-Making Techniques for Autonomous Vehicles

Increasing the automation level of autonomous vehicles is a research area in which most vehicle manufacturers and many research groups are involved. One of the key issues in autonomous vehicles is decision-making algorithms in order to react safely and efficiently to the environment. There are several decision architectures and techniques for planning, decision and control tasks. Decision-making Techniques for Autonomous Vehicles provides a general overview of control and decision-making tools that could be used in autonomous vehicles. Motion prediction and motion planning tools are presented, as well as the use of machine learning and adaptability to improve performance of these algorithms in real scenarios. Additionally, driver monitoring and behavior analysis are used for these tools to produce comprehensive and predictable reactions in the automated vehicle. Lastly, regulatory and ethical issues that must be considered for implementing correct and robust decision-making are presented. This book is for researchers as well as Masters and PhD students working in these areas of autonomous vehicles and decision algorithms. Provides a complete overview of decision making and control techniques for autonomous vehicles Includes technical, physical and mathematical explanations to provide knowledge for implementation of tools Features machine learning to improve decision-making algorithms performance Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios