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 planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with 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 performance of decision-making algorithms- Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios

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 planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with 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 performance of decision-making algorithms- Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios

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 planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with 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 performance of decision-making algorithms- Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios