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.
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Researchers and Masters' & PhD students working in the area of autonomous vehicles, control and decision making
Illustrationen
Approx. 200 illustrations; Illustrations
Maße
Höhe: 229 mm
Breite: 152 mm
Gewicht
ISBN-13
978-0-323-98339-6 (9780323983396)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Jorge Villagra graduated in Industrial Engineering at the Technical University of Madrid (UPM) in 2002. He received his PhD in Real-Time Computer Science, Robotics and Automatic Control at the Ecole des Mines de Paris (France) in 2006. From 2007 to 2009 he held a position of Visiting Professor at the University Carlos III (Spain). He then received a 3-year JAE Doc fellowship at the AUTOPIA Program in the Centre for Automation and Robotics, UPM-CSIC (Spain). From 2013 until 2016 he led the Department of ADAS and Autonomous Driving Systems at Ixion Industry & Aerospace SL. He is leading AUTOPIA Program at CSIC since October 2016.
He has participated in over 40 R&D), being the principal investigator in half of them. He is a regular speaker in various specialised forums, participates as an independent expert for the European Commission and has published more than 100 articles in international journals and conferences on connected and automated driving. Felipe Jimenez obtained his Master Degree in Industrial Engineering (Mechanics) from the Technical University of Madrid (UPM), his Master Degree in Automotive Engineering from the UPM, his Master Degree in Physical Science (Electronics and Automation) from the National University of Distance Education of Spain and his PhD in Mechanical Engineering from the UPM in 2001, 2002, 2005 and 2006, respectively.
Currently, he is Full Professor at the UPM and Research Deputy Director and Head of the Intelligent Systems Unit of the University Institute for Automobile Research of the UPM. His fields of interest are the automotive industry, vehicle safety, mechanical design, driver assistance systems and intelligent transport systems, mainly connected and automated driving. He has been involved in more than 40 R&D projects in the last 10 years and has developed engineering studies for relevant national firms. Furthermore, he is the leader of the Spanish Thematic Network on Intelligent Vehicles.
Herausgeber*in
Senior Scientist, Spanish National Research Council (CSIC), Madrid, Spain
Professor, Technical University of Madrid and Head of Intelligent Transports Systems, University Institute for Automobile Research, Madrid, Spain
1. Overview
PART I: EMBEDDED DECISION COMPONENTS
2. Embodied decision architectures
3. Behavior planning
4. Motion prediction and risk assessment
5. Motion search space
6. Motion planning
7. End-to-end architectures
8. Interplay between decision and control
PART II: INFRASTRUCTURE-ORIENTED DECISION-MAKING
9. Traffic data analysis and route planning
10. Cooperative driving
11. Infrastructure impact
PART III: USER INFLUENCE
12. Driver behavior
13. Human-machine interaction
PART IV: DEPLOYMENT ISSUES
14. Algorithms validation
15. Legal and social aspects
