Description
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
Table of Contents
1. OverviewPART I: EMBEDDED DECISION COMPONENTS2. Embodied decision architectures3. Behavior planning4. Motion prediction and risk assessment5. Motion search space6. Motion planning7. End-to-end architectures8. Interplay between decision and controlPART II: INFRASTRUCTURE-ORIENTED DECISION-MAKING9. Traffic data analysis and route planning10. Cooperative driving11. Infrastructure impactPART III: USER INFLUENCE12. Driver behavior13. Human-machine interactionPART IV: DEPLOYMENT ISSUES14. Algorithms validation15. Legal and social aspects
