Smart Transportation AI Enabled Mobility and Autonomous Driving

The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts.

The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.

1. Social Acceptance of Autonomous Driving – The Importance of Public Discourse and Citizen Participation 2. Artificial Intelligence for Autonomous Vehicle Fleets: Desired Requirements, Solutions and a Best Practice Project 3. Large-scale Simulation of a Mobility-on-Demand System for the German City Aachen 4. Artificial Intelligence for Fleets of Autonomous Vehicles: Desired Requirements and Solution Approaches 5. Future Urban Mobility: Designing New Mobility Technologies in Open Innovation Networks 6. Communication and Service Aspects of Smart Mobility: Improving Security, Privacy and Efficiency of Mobility Services by Utilizing Distributed Ledger Technology 7. Pseudonym Management through Blockchain: Cost-efficient Privacy Preservation on Smart Transportation 8. Simulation Platforms for Autonomous Driving and Smart Mobility: Simulation Platforms, Concepts, Software, APIs 9. Deep-Learning Based Depth Completion for Autonomous Driving Applications 10. Artificial Intelligence Based on Modular Reinforcement Learning and Unsupervised Learning 11. Functional Safety of Deep Learning Techniques in Autonomous Driving Systems

Guido Dartmann is Professor and Group Leader of the research area Distributed Systems and member of the Institute for Software Systems at the Environmental Campus, Trier University of Applied Sciences, Trier, Germany.

Guido Dartmann studied Computer Engineering at the RWTH Aachen University and received the Dipl.-Ing. in 2007. After his graduate studies, he became research assistant at the Institute of Communication Technology and Embedded Systems (ICE) of the RWTH Aachen University where he finished his PhD and received the Borchers Medal of the RWTH Aachen. In 2012, he became chief engineer at ICE. Since 02/2016, Prof. Dartmann is a full professor at Trier University of Applied Sciences.

His research interests include distributed systems, machine learning, data analytics, signal processing, optimization of technical systems, cyber-physical systems, wireless communication, cyber-security, internet of things, traffic and mobility. His research group at the Environmental Campus was granted by several grants of German federal ministries and European research. Prof. Dartmann is senior member of the IEEE and reviewer of several IEEE Transactions and he and he is co-author of more than 80 scientific papers in peer-reviewed journals, books and conference proceedings.

Prof. Dartmann is co-lead and member of the expert group Internet of Things at the national Information Technology Summit.

Anke Schmeink is Professor and Leader of the research area Information Theory and Systematic Design of Communication Systems (ISEK) at RWTH Aachen University, Aachen, Germany.

Anke Schmeink received the diploma degree in mathematics with a minor in medicine and the Ph.D. degree in electrical engineering and information technology from RWTH Aachen. She worked as a research scientist for Philips Research and is a professor at RWTH Aachen since 2012. Anke Schmeink is a senior member of IEEE, an editor for IEEE TWC and foun