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• Introduction: from highly automated to autonomous driving
• PART 1: Autonomous driving technology enablers 1. AI and software enablers for highly automated and autonomous vehicles 2. Communication advances for autonomous vehicles 3. Validation of autonomous vehicles: simulation, testing and certification
• PART 2: Autonomous driving from a business and market perspective 4. Consumers Demand for AVs: Driving forces and barriers for using AVs 5. A path of structural transformation for the automotive and insurance industries towards AVs 6. The impact of AVs on social welfare, GDP and economic productivity
• PART 3: User acceptance, security and ethics of autonomous driving 7. Why Ethics Matters for Autonomous Driving 8. Opportunities and Risks Associated with Autonomous Driving 9. User / public Acceptance of Autonomous Driving
• PART 4: Autonomous driving regulations, policies and standards 10. Regulatory bodies for highly automated and autonomous driving 11. Policies and policy making in autonomous driving 12. Autonomous driving, standardization bodies and standards
• PART 5: The future of autonomous driving and related applications 13. Autonomous driving applications 14. Conclusions and way forward.
• (source: Nielsen Book Data)

Autonomous Vehicles: Technologies, Regulations, and Societal Impacts explores both the autonomous driving concepts and the key hardware and software enablers, Artificial intelligence tools, needed infrastructure, communication protocols, and interaction with non-autonomous vehicles. It analyses the impacts of autonomous driving using a scenario-based approach to quantify the effects on the overall economy and affected sectors. The book assess from a qualitative and quantitative approach, the future of autonomous driving, and the main drivers, challenges, and barriers. The book investigates whether individuals are ready to use advanced automated driving vehicles technology, and to what extent we as a society are prepared to accept highly automated vehicles on the road. Building on the technologies, opportunities, strengths, threats, and weaknesses, Autonomous Vehicles: Technologies, Regulations, and Societal Impacts discusses the needed frameworks for automated vehicles to move inside and around cities. The book concludes with a discussion on what in applications comes next, outlining the future research needs.
(source: Nielsen Book Data)

• Autonomous Driving Landscape.- Computing Framework for Autonomous Driving.- Algorithm Deployment Optimization.- Systems Runtime Optimization.- Dataset and Benchmark.- Autonomous Driving Simulators.- Hardware Platforms.- Smart Infrastructure for Autonomous Driving.- Challenges and Open Problems.
• (source: Nielsen Book Data)

This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.
(source: Nielsen Book Data)

• Introduction
• Autonomous System Connectivity
• Embedded System Development
• Autonomous Vehicle Data Processing
• Vehicle Components
• Vehicle Navigation Computing
• Vehicle Test Drive and Simulation
• Advanced Chip Driven Electronics
• Sensors and Vision System Fusions
• V2V and V2X Communication
• Deep Learning and Neural Network Algorithms
• ADAS in Autonomous Driving.

This book introduces the technological innovations of robotic vehicles. It presents the concepts required for self-driving cars on the road. Besides, readers can gain invaluable knowledge in the construction, programming, and control of the six-legged robot. The book also presents the controllers and aerodynamics of several different types of rotorcrafts. It includes the simulation and flight of the various kinds of rotor-propelled air vehicles under each of their different aerodynamics environment. The book is suitable for academia, educators, students, and researchers who are interested in autonomous vehicles, robotics, and rotor-propelled vehicles.
(source: Nielsen Book Data)

• Autonomous vehicles and mobility impacts on transit and freight: factors affecting adoption, challenges, and opportunities / P.S. Sriraj
• Demanding a better transportation future through automation / Austin Brown
• Are we there yet, and where is it we need to go? Myths and realities of connected and autonomous vehicles / Stan Caldwell and Chris Hendrickson
• Automation of freight systems / Kazuya Kawamura
• Back to the future: discussing our new autonomous reality / Taylor Long.

"Autonomous vehicle (AV) technology represents a possible paradigm shift in our way of life. But complex challenges and obstacles impose a reality at odds with the utopian visions propounded by AV enthusiasts in the private and public sectors. This book examines the technological questions still surrounding autonomous vehicles and the uncertain societal and legislative impact of widespread AV adoption. Assessing both short- and long-term concerns, the authors probe how autonomous vehicles might change transportation but also land use, energy consumption, mass transit, commuter habits, traffic safety, job markets, the freight industry, and supply chains. At the same time, the essays discuss opportunities for industry, researchers, and policymakers to make the autonomous future safer, more efficient, and more mobile"-- Provided by publisher.

• Acknowledgements; Contents; Abstract; Kurzfassung; Nomenclature; 1 Introduction; 2 Sensor Data Preprocessing; 3 Volumetric Hybrid Map-based SLAM; 4 Geometric Model-based Tracking with Multiple Motion Models; 5 Evaluation; 6 Conclusion; A Appendix; List of Publications; Bibliography.

• Development of Hybrid Brake-by-Wire System for Autonomous Vehicle.- Current Study on Hybrid Brake-by-Wire System for Autonomous Vehicle.- Modeling of Hybrid Brake-by-Wire System for Autonomous Vehicle.- Design of Brake-by-Wire System for Autonomous Vehicle.- Braking Force Distribution Control of Brake-by-Wire System.- Mode Switching of Hybrid Brake-by-Wire System.
• (source: Nielsen Book Data)

This book establishes the models of the electric motor, the hydraulic compound brake system, and the electromagnetic and friction integrated brake system. Considering the two principles on safety and energy saving, it proposes a hybrid brake-by-wire system optimization design method and proposes the optimization method of braking force distribution in different braking modes. The methodology of the book is by using the common Lyapunov function to analyze the stability of the braking mode switching process and designs the braking mode switching controller of the hybrid braking system. The selection of materials provides readers with some guidance in the future design and control of hybrid drive-by-wire systems for autonomous vehicles.
(source: Nielsen Book Data)

• 1. Introduction to Automotive Cybersecurity 1.1 Overview 1.2 Introduction 1.2.1. Security and Its Impact 1.3. Cybersecurity in Automotive Technology 1.3.1 The Rising Threat 1.4. Vehicular Ransomware Attack 1.4.1. Vehicle Ransomware Attack Scheme 1.5. Overview of Topics 1.5.1 Intelligent and Autonomous Vehicle 1.5.2 Cybersecurity and Privacy in Intelligent Autonomous Vehicles 1.5.3 In-Vehicle communication and Cyber Security 1.5.4 AUTOSAR Embedded Security in Vehicles 1.5.5 Inter-vehicle Communication and Cyber Security 1.5.6 Internet of Vehicles, Vehicular Social Networks, and Security Issues 1.5.7 V2X Current Security Issues, Standards, Challenges, Use cases and Future Trends
• 2. Intelligent Autonomous Vehicles
• 2.1 Overview 2.2 History of Intelligent and Autonomous Vehicle 2.3 Classification of Autonomous vehicle driving levels 2.3.1 SAE and NHTS classification: 2.4 State-of-the-art Intelligent and Autonomous Vehicle Technologies 2.4.1 Autonomous Vehicle 2.4.1.1 In-vehicle Communication 2.4.1.2. In-Vehicle Networking Types 2.4.2 Connected Vehicle Technology 2.4.2.1 Inter-vehicle communication 2.5 Battle for Adoption 2.6 Market demand of Automotive Cyber security 2.7 Summary
• 3. Security and Privacy in Intelligent Autonomous Vehicles 3.1 Overview 3.2 Cryptography Introduction 3.3 Cryptography Objective 3.3.1 Confidentiality 3.3.2 Data Integrity 3.3.3 Authentication 3.3.4 Non-Repudiation 3.4 Cryptographic Primitives 3.4.1 Symmetric Key or Secret Key encryption 3.4.2 Asymmetric Key or Public Key encryption 3.4.3 Digital Signatures 3.4.4 Homomorphic Encryption 3.5 Cyber Security in Intelligent and Autonomous Vehicles 3.5.1. Cyber Security Framework 3.5.2. Cybersecurity Layers by Design 3.5.3. Threat Modelling Method (TMM) 3.5.4. HARA and TARA Safety and Security Methods 3.5.5 Security & Privacy Threats in Vehicular Networks
• 3.5.6 Autonomous Vehicle Cyber Security
• 3.5.7 Connected Vehicle Security
• 3.5.7.1 Components of IAV
• 3.5.7.2 Vehicular Ad-hoc Networks Vulnerabilities
• 3.5.7.3 Privacy in VANET
• 3.5.8. Trust Management in VANET 3.5.6. Blockchain as a security in VANET 3.6. Summary
• 4. In-vehicle Communication and Cyber Security 4.1 Overview 4.2. In-Vehicle System 4.2.1 Vehicle Electrical and Electronic System 4.2.1.1 Body electronics 4.2.1.2 Chassis Electronics 4.3. In-Vehicle Communication 4.3.1 In-vehicle Sensing Technologies 4.3.2 In-Vehicle Network (IVN) Systems 4.2.1.3 Comfort Electronics 4.2.1.4 Electronic Control Units (ECUs): 4.2.1.5 Infotainment Electronics: 4.2.1.6 Sensor Technology 4.2.1.7 Advanced Driver Assistance System (ADAS) 4.3. In-vehicle communication 4.3.1 In-Vehicle Sensing Technologies 4.3.1.1. Sensor Technologies 4.3.1.2 Vision Technologies 4.3.1.3 Positioning Technologies 4.3.2 In-Vehicle Network (IVN) Systems 4.3.2.1 Controller Area Network (CAN) 4.3.2.2 FlexRay 4.3.2.3 Local Interconnect Network (LIN) 4.3.2.4 Automotive Ethernet (AE) 4.3.2.5 Media Oriented Serial Transport (MOST) 4.3.2.6 Single Edge Nibble Transmission SENT (SENT) 4.3.2.7 Other BUS protocols 4.4 In-Vehicle Network Architecture and Topology 4.5 Functional Safety and Cybersecurity 4.6 In-Vehicle Cybersecurity Issues and Challenges 4.6.1 Challenges of IVN architecture 4.6.1.1 Bandwidth 4.6.1.2 Latency 4.6.1.3 Reliability 4.6.2 In-Vehicle Onboard Ports, Threats and Countermeasures 4.6.2.1 OBD-II ports 4.6.2.2 USB port: 4.6.2.3 Electric Vehicle (EV) Charging port 4.6.2.4 Countermeasures to port threats 4.7 Cyber Security in In-Vehicle Network 4.7.1 In-Vehicle Network (IVN) Security Threats 4.7.1.1 CAN Security 4.7.1.2 FlexRay Security Threats 4.7.1.3 LIN Security Threats 4.7.1.4 AE Security 4.7.1.5 MOST Security Threats 4.7.2 Cyber Security protection Layers 4.7.3 Cyber security for ECU 4.7.3.1 On communication 4.7.3.2 On Exposed Functions 4.7.3.3 Non-Invasive 4.7.3.4 Semi-Invasive 4.7.3.5 Invasive 4.8 Summary
• 5 AUTOSAR Embedded Security in Vehicles 5.1 Overview 5.2 Introduction 5.2.1 Background 5.2.1.1 TARA 5.2.1.2 STRIDE 5.2.1.3 AUTOSAR 5.3 Threat models for the automotive domain 5.3.1 Adaptation of TARA
• 5.3.2 Adaptation of STRIDE 5.4 Applying the Adapted Threat Models to the Automotive Domain 5.4.1 TARA 5.4.2 STRIDE 5.5 Results 5.5.1 TARA 5.5.2 STRIDE 5.5.3 Related Work 5.5.4 Discussion and Future Work 5.6 Conclusion
• 6. Connected Vehicle Technology and Cyber Security 6.1 Overview 6.2 Connected Vehicles 6.2.1 VANET Technology Overview 6.2.2 Types of communications Technology in Connected Vehicle 6.3 State-of-the-art Technologies in VANET 6.3.1 DSRC based V2X 6.3.2 Cellular based V2X 6.3.2.1 Advancement in Cellular V2X 6.3.3 Hybrid V2X Technology
• 6.3.4 C-V2X applications and requirements 6.4 Role of Edge computing and SDN in V2X communication 6.5 Connected Vehicle Cyber Security 6.5.1. WAVE Communication Cybersecurity 6.5.2. Security and Privacy in V2X communication 6.6. Trust management in V2X communication 6.7. Homomorphic Encryption in V2X communication 6.8. Blockchain in V2X communication 6.9. Safety Standards for IAV 6.10. Summary
• 7. Internet of Vehicles, Vehicular Social Networks, and Cybersecurity 7.1 Overview 7.2 IoT in VANET (IoV) 7.2.1 IoV Network Model 7.2.1.1 Cloud network 7.2.1.2 Communication Network 7.2.1.3 Vehicles 7.2.2 IoV Layered Architecture 7.2.3 Security in IoV 7.2.4 IoV security requirements and attacks 7.2.5 Challenges in IoV 7.2.6 IoV Applications 7.3 Machine Learning in Vehicular Networks 7.3.1 Types of Machine Learning Techniques 7.3.2 Machine Learning as a Cybersecurity Solutions in Vehicular Networks 7.3.3 Application of Machine Learning in Vehicular Networks 7.4 Security based on ML in Vehicular Networks 7.4.3 ML to detect Misbehaviour Detection 7.4.4 ML to detect DoS and DDoS Attacks 7.4.5 ML to detect Intrusion attack 7.4.6 ML to detect Smart Jamming Attack 7.5 Vehicular Social Network 7.5.1 Applications of VSN 7.5.2 Security Issues 7.5.3 Privacy Issues 7.6 Summary
• 8. V2X Standards, Regulations, Security Issues, Use Cases and Future Trends 8.1 Overview 8.2. Standards, regulations and legal issues 8.2.1 International Cybersecurity Standardization in Automotive industry 8.2.2 Standardization for V2X Communication and Frequency Allocation 8.2.2.1 International V2X Standardization
• 8.2.3 ITS Spectrum Recommendation and Regulation Consideration
• 8.2.4 Cyber Security Standardization in V2X 8.3 Competition over V2X technology Adoption 8.3.1 Challenges for DSRC V2X and Cellular V2X 8.4 V2X Use Cases 8.4.1 Smart mobility 8.4.1.1 Smart Mobility and Security Issues 8.4.2 V2X Testbed 8.5 Current Trends and Future of Intelligent and Autonomous Vehicles 8.5.1 Trends in Intelligent and Autonomous Vehicles 8.5.2 Autonomous Electric Vehicle and Challenges 8.5.3 Cyber-attacks in Future Autonomous Vehicles 8.5.2.1 Protection of Future Autonomous Vehicle from Cyber attacks 8.5.2.2. Cybersecurity of Future Vehicles and Machine Learning Techniques 8.5.4 Challenges in Future Autonomous Vehicles 8.5.5 Intelligent Autonomous Vehicle Improves Environment 8.6 Summary.
• (source: Nielsen Book Data)

This book outlines the development of safety and cybersecurity, threats and activities in automotive vehicles. This book discusses the automotive vehicle applications and technological aspects considering its cybersecurity issues. Each chapter offers a suitable context for understanding the complexities of the connectivity and cybersecurity of intelligent and autonomous vehicles. A top-down strategy was adopted to introduce the vehicles' intelligent features and functionality. The area of vehicle-to-everything (V2X) communications aims to exploit the power of ubiquitous connectivity for the traffic safety and transport efficiency. The chapters discuss in detail about the different levels of autonomous vehicles, different types of cybersecurity issues, future trends and challenges in autonomous vehicles. Security must be thought as an important aspect during designing and implementation of the autonomous vehicles to prevent from numerous security threats and attacks. The book thus provides important information on the cybersecurity challenges faced by the autonomous vehicles and it seeks to address the mobility requirements of users, comfort, safety and security. This book aims to provide an outline of most aspects of cybersecurity in intelligent and autonomous vehicles. It is very helpful for automotive engineers, graduate students and technological administrators who want to know more about security technology as well as to readers with a security background and experience who want to know more about cybersecurity concerns in modern and future automotive applications and cybersecurity. In particular, this book helps people who need to make better decisions about automotive security and safety approaches. Moreover, it is beneficial to people who are involved in research and development in this exciting area. As seen from the table of contents, automotive security covers a wide variety of topics. In addition to being distributed through various technological fields, automotive cybersecurity is a recent and rapidly moving field, such that the selection of topics in this book is regarded as tentative solutions rather than a final word on what exactly constitutes automotive security. All of the authors have worked for many years in the area of embedded security and for a few years in the field of different aspects of automotive safety and security, both from a research and industry point of view. .
(source: Nielsen Book Data)

• Energy saving potentials of CAVs
• Fundamentals of vehicle modeling
• Perception and Control for Connected and Automated Vehicles
• Route and traffic description
• Energy-efficient route navigation (Eco-routing)
• Energy-efficient speed profiles (Eco-driving)
• Specific scenarios and applications
• Eco-driving Practical Implementation
• Detailed Case Studies
• Parametric optimization method for eco-driving of ICEVs
• Domain of Feasibility of the Analytical Optimal Speed Profiles for EVs.

This book elaborates the science and engineering basis for energy-efficient driving in conventional and autonomous cars. After covering the physics of energy-efficient motion in conventional, hybrid, and electric powertrains, the book chiefly focuses on the energy-saving potential of connected and automated vehicles. It reveals how being connected to other vehicles and the infrastructure enables the anticipation of upcoming driving-relevant factors, e.g. hills, curves, slow traffic, state of traffic signals, and movements of nearby vehicles. In turn, automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and to save energy. Lastly, the energy-efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. Building on classical methods of powertrain modeling, optimization, and optimal control, the book further develops the theory of energy-efficient driving. In addition, it presents numerous theoretical and applied case studies that highlight the real-world implications of the theory developed. The book is chiefly intended for undergraduate and graduate engineering students and industry practitioners with a background in mechanical, electrical, or automotive engineering, computer science or robotics.

• Introduction: Not If but When
• Chapter 1: Futurama 1: New Horizons
• Chapter 2: Futurama 2: Magic Highway, USA
• Chapter 3: Futurama 3: From CenterCore to Demo '97
• Chapter 4: Futurama 4: Autonorama
• Chapter 5: Data Don't Drive Conclusion: We Already Have What We Need Acknowledgments About the Author Endnotes Index.
• (source: Nielsen Book Data)

"The foundation has been laid for fully autonomous, " Elon Musk announced in 2016, when he assured the world that Tesla would have a driverless fleet on the road in 2017. "It's twice as safe as a human, maybe better." Promises of techno-futuristic driving utopias have been ubiquitous wherever tech companies and carmakers meet. In Autonorama: The Illusory Promise of High-Tech Driving, technology historian Peter Norton argues that driverless cars cannot be the safe, sustainable, and inclusive "mobility solutions" that tech companies and automakers are promising us. The salesmanship behind the driverless future is distracting us from investing in better ways to get around that we can implement now. Unlike autonomous vehicles, these alternatives are inexpensive, safe, sustainable, and inclusive. Norton takes the reader on an engaging ride -from the GM Futurama exhibit to "smart" highways and vehicles-to show how we are once again being sold car dependency in the guise of mobility. He argues that we cannot see what tech companies are selling us except in the light of history. With driverless cars, we're promised that new technology will solve the problems that car dependency gave us-zero crashes! zero emissions! zero congestion! But these are the same promises that have kept us on a treadmill of car dependency for 80 years. Autonorama is hopeful, advocating for wise, proven, humane mobility that we can invest in now, without waiting for technology that is forever just out of reach. Before intelligent systems, data, and technology can serve us, Norton suggests, we need wisdom. Rachel Carson warned us that when we seek technological solutions instead of ecological balance, we can make our problems worse. With this wisdom, Norton contends, we can meet our mobility needs with what we have right now.
(source: Nielsen Book Data)

• The transformation of the time-honored auto industry.- Basic technologies for autonomous driving.- Applications of autonomous driving.- EV: The best platform for autonomous driving.- Infrastructures matter.- Ten challenges facing autonomous driving.- Overseas players: chasing the trend.- China won't want to lag behind.- A bright future is foreseeable.
• (source: Nielsen Book Data)

This book systematically discusses the development of autonomous driving, describing the related history, technological advances, infrastructure, social impacts, international competition, China's opportunities and challenges, and possible future scenarios. This popular science book uses straightforward language and includes quotes from ancient Chinese poems to enhance the reading experience. The discussions are supplemented by theoretical elaborations, presented in tables and figures. The book is intended for auto fans, upper undergraduate and graduate students in the field of automotive engineering.
(source: Nielsen Book Data)

• Intro
• Title
• Copyright
• Contents
• About the editors
• Preface
• 1 Introduction
• 1.1 Scope of the book
• 1.2 Advanced, safe, secure, and efficient mobility
• 1.3 Governance: policies and regulations
• 1.4 Social equity and justice
• 1.5 Sustainability: environmental and financial factors
• 1.6 Planning: transportation system and land use
• 1.7 Design, operations, and management
• 1.8 Implementation context, urban development, and other impacts
• 1.9 Adapting to the long-term effects of the global pandemic
• 1.10 Contents of the book
• 1.11 Audience for the book

• Author contributions
• Declaration of conflicting interests
• References
• 2 Navigating seismic shifts in transportation
• 2.1 Setting the stage: trends disrupting mobility
• 2.2 Converging innovations
• 2.3 Shared mobility, shared micromobility, and last mile delivery
• 2.4 Electrification and automation
• 2.5 Digital information, fare integration, and the commodification of transportation (digitization)
• 2.5.1 Mobility on demand (MOD)
• 2.5.2 Mobility as a Service (MaaS)
• 2.5.3 Differences between MOD and MaaS
• 2.6 Conclusion
• Author contributions

• Declaration of conflicting interests
• References
• 3 Policy and regulatory environment: shared automated vehicles
• 3.1 Introduction
• 3.1.1 Levels of automation
• 3.1.2 Four-phase transition framework
• 3.1.3 Current U.S. AV policies
• 3.2 Federal
• 3.3 State
• 3.4 Local
• 3.5 Passenger safety
• 3.6 Data sharing
• 3.7 Mitigating externalities
• 3.7.1 Mitigating for congestion, VMT/VKT, and emissions
• 3.7.2 Social equity
• 3.7.3 Labor implications
• 3.8 SAV ownership and business model considerations
• 3.9 Public transit and SAVs

• 3.10 Policy options for federal, state, and local governments
• 3.10.1 Federal
• 3.10.2 State
• 3.10.3 Local
• 3.11 Conclusion
• Author contributions
• Declaration of conflicting interests
• References
• 4 Concept level designs: high-level architecture
• 4.1 Introduction
• 4.2 Shared mobility components and a general architecture
• 4.3 Shared mobility systems and their architectures
• 4.3.1 Incumbent shared mobility systems
• 4.3.2 Innovative shared mobility systems
• 4.4 Data architecture and management
• 4.4.1 Generalized data architecture
• 4.4.2 Data management
• 4.5 Summary

• Author contributions
• Declaration of conflicting interests
• References
• 5 Shared mobility: managing rights-of-way, developer incentives, and planning principles
• 5.1 Shared mobility and the public rights-of-way
• 5.1.1 Allocating rights-of-way and curb space
• 5.2 Shared mobility and the development process
• 5.2.1 Parking reductions and substitution
• 5.2.2 Density bonuses
• 5.2.3 Incentive zoning in practice
• 5.3 Shared mobility and the planning process
• 5.4 Shared mobility and the built environment
• 5.5 Stakeholder and public involvement
• 5.6 Conclusion

• Author contributions.

Shared mobility is gaining increasing attention in private and public sectors. Serving as a source of information on how best to shape shared vehicle systems of the future, this book contributes knowledge on key facets of shared mobility. It includes shared vehicle systems as well as shared automated vehicle systems.

• Chapter 1: Introduction
• Chapter 2: Navigating seismic shifts in transportation
• Chapter 3: Policy and regulatory environment: shared automated vehicles
• Chapter 4: Concept level designs: high-level architecture
• Chapter 5: Shared mobility: managing rights-of-way, developer incentives, and planning principles
• Chapter 6: Shared mobility services: prioritizing social good
• Chapter 7: Multimodal relationships: shared and automated vehicles and high-capacity public transit
• Chapter 8: Design of systems with nonautomated electric vehicles
• Chapter 9: Design of systems with automated and electric vehicles
• Chapter 10: Demand for shared mobility to complement public transportation: human-driven and automated vehicles
• Chapter 11: Demand for shared mobility to replace private mobility using connected and automated vehicles
• Chapter 12: Matching demand and supply under uncertainty
• Chapter 13: Operations and management
• Chapter 14: Impacts on the public realm: understanding the context
• Chapter 15: Impacts on the public realm: the potential for a positive outcome
• Chapter 16: Economic factors
• Chapter 17: The impacts of shared and automated mobility
• Chapter 18: Future directions: maximizing the social and environmental benefits of shared and automated mobility services Glossary of technical terms and acronyms Literature for further readings.
• (source: Nielsen Book Data)

Shared vehicles are a key part of any future intelligent and clean transport system, as they can allow for the sharing and potentially more efficient use of transport resources and fuel. Shared mobility has been gaining attention in the private and public sectors as a possible strategy for taming auto ownership, vehicle miles/kilometers travelled, and emissions. Serving as a source of information on how best to shape shared vehicle systems of the future, this book contributes knowledge on key facets of shared mobility. It includes shared vehicle systems as well as shared automated vehicle systems. Themes covered in the book include policy and regulatory frameworks, planning, design, technology, demand and supply models, algorithms, operations, management, economic factors, business models, social equity, environmental impacts, and pandemic effects. Shared Mobility and Automated Vehicles: Responding to socio-technical changes and pandemics comprehensively and systematically covers this important topic for an audience of researchers in academia and research institutes involved with intelligent transport systems and urban mobility. The book is also a valuable resource for public policy analysts, planners, system designers, system level technology developers, consultants, and students.
(source: Nielsen Book Data)

• Contents: Acknowledgments
• Part 1: Introduction and Background 1. Introduction 2. Autonomous-Vehicle Operations and the Process of Adoption
• Part 2: Potential Effects of Autonomous Vehicles 3. The Potential Effects of Autonomous Vehicles on Economic Sectors 4. Estimating the Effects of Congestion on Economic-Performance Measures 5. Estimation Results Obtained from the Congestion Model 6. Simulation of the Effects of Autonomous Vehicles on Congestion 7. Other Important Effects of Autonomous Vehicles
• Part 3: Constraints on the Success of Autonomous Vehicles 8. Technological Constraints 9. Public-Policy Constraints 10. Conclusion Appendix: Measuring Freight Flows across Urban Areas Notes References Index.
• (source: Nielsen Book Data)

Better public policies can make the road smoother for self-driving vehicles and the society that soon will depend on them.Whether you find the idea of autonomous vehicles to be exciting or frightening, the truth is that they will soon become a significant everyday presence on streets and highways not just a novel experiment attracting attention or giggles and sparking fears of runaway self-driving cars. The emergence of these vehicles represents a watershed moment in the history of transportation. If properly encouraged, this innovation promises not only to vastly improve road travel and generate huge benefits to travelers and businesses, but to also benefit the entire economy by reducing congestion and virtually eliminating vehicle accidents. The impacts of autonomous vehicles on land use, employment, and public finance are likely to be mixed. But widely assumed negative effects are generally overstated because they ignore plausible adjustments by the public and policymakers that could ameliorate them. This book by two transportation experts argues that policy analysts can play an important and constructive role in identifying and analyzing important policy issues and necessary steps to ease the advent of autonomous vehicles. Among the actions that governments must take are creating a framework for vehicle testing, making appropriate investments in the technology of highway networks to facilitate communication involving autonomous vehicles, and reforming pricing and investment policies to enable operation of autonomous vehicles to be safe and efficient. The authors argue that policymakers at all levels of government must address these and other issues sooner rather than later. Prompt and effective actions outlined in this book are necessary to ensure that autonomous vehicles will be safe and efficient when the public begins to adopt them as replacements for current vehicles.
(source: Nielsen Book Data)

• Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review.- Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment.- Reactive Obstacle Avoidance Method for a UAV.- Guaranteed Performances for Learning-Based Control Systems using Robust Control Theory.- A cascaded deep Neural Network for Position Estimation of Industrial Robots.- Managing Deep Learning Uncertainty for Autonomous Systems.- Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning.- Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-Grids.- Reinforcement learning for Autonomous Morphing Control and Cooperative Operations of UAV Cluster.- Image-Based Identification of Animal Breeds Using Deep Learning.
• (source: Nielsen Book Data)

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.
(source: Nielsen Book Data)

• Fundamentals on Vehicle and Tyre Modelling.- Vehicle Steering and Suspension Kinematics/Compliance and their Relationship to Vehicle Performance.- Tyre Mechanics and Thermal Effects on Tyre Behaviour.- Torque Vectoring Control for Enhancing Vehicle Safety and Energy Effciency.- State and Parameter Estimation for Vehicle Dynamics.- Automated Driving Vehicles.
• (source: Nielsen Book Data)

• Tyre Mechanics and Thermal Effects on Tyre Behaviour
• 1 Introduction to the Tyre
• 2 Tyre Structure
• 3 Mechanics of Tyres
• 4 Tyre Role in Vehicle Dynamics
• 5 Tyre Working Conditions Effects
• 6 Tyre Thermal Modelling in Vehicle Dynamics and Driving Simulations
• 7 Tyre Wear Modelling
• References
• Torque Vectoring Control for Enhancing Vehicle Safety and Energy Efficiency
• 1 Introduction
• 2 Torque Vectoring Control Framework
• 3 Reference Generator: rref, (βref)
• 3.1 Fundamentals on the Design of rref
• 3.2 Design of the Full Vehicle Cornering Response and Driving Modes

This book examines the fundamentals of vehicle dynamics, as well as the recent trends in the field, such as torque vectoring control, vehicle state estimation, and autonomous vehicles. It investigates the most pressing problems that vehicle dynamics engineers have been facing nowadays, and the challenges of autonomous vehicles in terms of perception, path planning, and analysis of the road environment. The book will serve as a useful tool for graduate students and researchers in vehicle dynamics and control.
(source: Nielsen Book Data)

• Section I
• Man and Machine
• Section II
• Mobility
• Section III
• Traffic
• Section IV
• Safety and Security
• Section V
• Law and Liability
• Section VI
• Acceptance.

This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of ℓ́ℓautonomous driving."

• Preface.- Importance of pasture grassland and its careful maintenance.- State of the art on grazing farms and the trend of automation in agriculture.- Added value of pasture grazing by automated pasture care.- Potential technologies for the automation of pasture care.- Requirements for automation of pasture care by a mobile machine.- Evaluation, selection and development of components for pasture care automation.- Modeling the machine.- Development of machine control.- Evaluation of the developed machine under real conditions.- Summary and outlook.
• (source: Nielsen Book Data)

This book examines the possibility of automating pasture care by fusing conventional technologies with modern sensor technologies, including the accompanying electrification. It subsequently explores the feasibility and benefits of such a system on the basis of a prototype. The overall challenge in fodder production, and in milk and meat production, is to shift the focus away from the economic aspects and toward achieving a better balance with ecological and societal aspects. In the future, pastureland will become an increasingly valuable resource. Good pasture turf is the basis of high grazing performance and an efficient grazing farm; reduced quantity and quality of pasture forage are chiefly due to insufficient pasture care. The prototype developed and discussed here, based on a commercially available remote-controlled mulcher, performs the selective pasture maintenance needed for precision farming. The vehicle has been upgraded with a GPS system for automatic guidance, while a 2D laser scanner is used to localise relevant spots in real-time. The pasture maintenance operations include mulching of un-grazed spots and reseeding of damage done by footsteps. The book presents the results of field tests on effective spot detection and the fuel-saving benefits of selective mulching.
(source: Nielsen Book Data)

• Background and State of the Art.- Diagnosis of Electrical Faults in Electric Machines.- Diagnosis of Mechanical Faults in Electric Machines.
• (source: Nielsen Book Data)

Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models.
(source: Nielsen Book Data)

Self-driving cars sound fantastical and futuristic and yet they'll soon be on every street in America. Whether it's Tesla's Autopilot, Google's Waymo, Mercedes's Distronic, or Uber's modified Volvos, companies around the world are developing autonomous cars. But why? And what will they mean for the auto industry and humanity at large? In Robot, Take the Wheel, famed automotive expert Jason Torchinsky gives a colorful account of the development of autonomous vehicles and their likely implications. Torchinsky encourages us to think of self-driving cars as an entirely new machine, something beyond cars as we understand them today. He considers how humans will get along with these robots that will take over our cars' jobs, what they will look like, what sorts of jobs they may do, what we can expect of them, how they should act, ethically, how we can trick them and have fun with them, and how we can make sure there's still a place for those of us who love to drive, especially with a manual transmission. This vibrant volume brims with insider information. It explores what's ahead and considers what we can do now to shape the automated future.