Decision-making Techniques for Autonomous Vehicles

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 INDICE: I. INTRODUCTION1. Overview2. Historical perspectiveII. DECISION AND CONTROLA. Embedded decision tools3. Embodied Decision architectures4. Motion search space5. Behavior generation and planning6. Motion prediction7. Motion planning8. Risk assessment9. Machine learning and adaptability10. Interplay between decision and controlB. Infrastructure-oriented decision making11. Route planning12. Cooperative driving13. Infrastructure impactIII. USER AND SOCIAL INFLUENCEA. User influence14. Driver behavior15. Human-machine interaction in autonomous vehiclesB. Implementation issues16. Algorithms evaluation 17. Social and legal aspects

• ISBN: 978-0-323-98339-6
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 400
• Fecha Publicación: 01/02/2023
• Nº Volúmenes: 1
• Idioma: Inglés