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About this book

This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation.
The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF.
Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.

Table of Contents

Frontmatter

2019 | OriginalPaper | Chapter

Chapter 1. Cooperation and the Role of Autonomy in Automated Driving

Gina Wessel, Eugen Altendorf, Constanze Schreck, Yigiterkut Canpolat, Frank Flemisch

2019 | OriginalPaper | Chapter

Chapter 2. Robust Real-World Emissions by Integrated ADF and Powertrain Control Development

Frank Willems, Peter van Gompel, Xander Seykens, Steven Wilkins

2019 | OriginalPaper | Chapter

Chapter 3. Gaining Knowledge on Automated Driving’s Safety—The Risk-Free VAAFO Tool

Philipp Junietz, Walther Wachenfeld, Valerij Schönemann, Kai Domhardt, Wadim Tribelhorn, Hermann Winner

2019 | OriginalPaper | Chapter

Chapter 4. Statistical Model Checking for Scenario-Based Verification of ADAS

Sebastian Gerwinn, Eike Möhlmann, Anja Sieper

2019 | OriginalPaper | Chapter

Chapter 5. Game Theory-Based Traffic Modeling for Calibration of Automated Driving Algorithms

Nan Li, Mengxuan Zhang, Yildiray Yildiz, Ilya Kolmanovsky, Anouck Girard

2019 | OriginalPaper | Chapter

Chapter 6. A Virtual Development and Evaluation Framework for ADAS—Case Study of a P-ACC in a Connected Environment

Harald Waschl, Roman Schmied, Daniel Reischl, Michael Stolz

2019 | OriginalPaper | Chapter

Chapter 7. A Vehicle-in-the-Loop Emulation Platform for Demonstrating Intelligent Transportation Systems

Wynita Griggs, Rodrigo Ordóñez-Hurtado, Giovanni Russo, Robert Shorten

2019 | OriginalPaper | Chapter

Chapter 8. Virtual Concept Development on the Example of a Motorway Chauffeur

G. Nestlinger, A. Rupp, P. Innerwinkler, H. Martin, M. Frischmann, J. Holzinger, G. Stabentheiner, M. Stolz

2019 | OriginalPaper | Chapter

Chapter 9. Automation of Road Intersections Using Distributed Model Predictive Control

Alexander Katriniok, Peter Kleibaum, Martina Joševski

2019 | OriginalPaper | Chapter

Chapter 10. MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation

Alex G. Cunningham, Enric Galceran, Dhanvin Mehta, Gonzalo Ferrer, Ryan M. Eustice, Edwin Olson
Additional information