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