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Artificial Intelligence for Autonomous Vehicles : The Future of Driverless Technology
Artificial Intelligence for Autonomous Vehicles : The Future of Driverless Technology
Autore Rajendran Sathiyaraj
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (268 pages)
Disciplina 629.2/046028563
Altri autori (Persone) SabharwalMunish
HuYu-Chen
BalusamyBalamurugan
Collana Advances in Data Engineering and Machine Learning Series
Soggetto topico Automated vehicles - Technological innovations
ISBN 9781119847656
1119847656
9781119847649
1119847648
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence in Autonomous Vehicles-A Survey of Trends and Challenges -- 1.1 Introduction -- 1.2 Research Trends of AI for AV -- 1.3 AV-Pipeline Activities -- 1.3.1 Vehicle Detection -- 1.3.2 Rear-End Collision Avoidance -- 1.3.3 Traffic Signal and Sign Recognition -- 1.3.4 Lane Detection and Tracking -- 1.3.5 Pedestrian Detection -- 1.4 Datasets in the Literature of Autonomous Vehicles -- 1.4.1 Stereo and 3D Reconstruction -- 1.4.2 Optical Flow -- 1.4.3 Recognition and Segmentation of Objects -- 1.4.4 Tracking Datasets -- 1.4.5 Datasets for Aerial Images -- 1.4.6 Sensor Synchronization Datasets -- 1.5 Current Industry Standards in AV -- 1.6 Challenges and Opportunities in AV -- 1.6.1 Cost -- 1.6.2 Security Concerns -- 1.6.3 Standards and Regulations -- 1.7 Conclusion -- References -- Chapter 2 Age of Computational AI for Autonomous Vehicles -- 2.1 Introduction -- 2.1.1 Autonomous Vehicles -- 2.1.2 AI in Autonomous Vehicles -- 2.1.2.1 Functioning of AI in Autonomous Vehicles -- 2.2 Autonomy -- 2.2.1 Autonomy Phases -- 2.2.2 Learning Methodologies for Incessant Learning in Real-Life Autonomy Systems -- 2.2.2.1 Supervised Learning -- 2.2.2.2 Unsupervised Learning -- 2.2.2.3 Reinforcement Learning -- 2.2.3 Advancements in Intelligent Vehicles -- 2.2.3.1 Integration of Technologies -- 2.2.3.2 Earlier Application of AI in Automated Driving -- 2.3 Classification of Technological Advances in Vehicle Technology -- 2.4 Vehicle Architecture Adaptation -- 2.5 Future Directions of Autonomous Driving -- 2.6 Conclusion -- References -- Chapter 3 State of the Art of Artificial Intelligence Approaches Toward Driverless Technology -- 3.1 Introduction -- 3.2 Role of AI in Driverless Cars -- 3.2.1 What is Artificial Intelligence? -- 3.2.2 What are Autonomous Vehicles?.
3.2.3 History of Artificial Intelligence in Driverless Cars -- 3.2.4 Advancements Over the Years -- 3.2.5 Driverless Cars and the Technology they are Built Upon -- 3.2.6 Advancement of Algorithms -- 3.2.7 Case Study on Tesla -- 3.3 Conclusion -- References -- Chapter 4 A Survey on Architecture of Autonomous Vehicles -- 4.1 Introduction -- 4.1.1 What is Artificial Intelligence? -- 4.1.2 What are Autonomous Vehicles? -- 4.2 A Study on Technologies Used in AV -- 4.2.1 Artificial Vision -- 4.2.2 Varying Light and Visibility Conditions -- 4.2.3 Scenes with a High Dynamic Range (HDR) -- 4.2.3.1 3 Dimensional Technology -- 4.2.3.2 Emerging Vision Technologies -- 4.2.4 Radar -- 4.2.4.1 Emerging Radar Technologies -- 4.2.5 LiDAR -- 4.2.5.1 Emerging LiDAR Technologies -- 4.3 Analysis on the Architecture of Autonomous Vehicles -- 4.3.1 Hardware Architecture -- 4.3.2 Software Architecture -- 4.4 Analysis on One of the Proposed Architectures -- 4.5 Functional Architecture of Autonomous Vehicles -- 4.6 Challenges in Building the Architecture of Autonomous Vehicles -- 4.6.1 Road Condition -- 4.6.2 Weather Condition -- 4.6.3 Traffic Condition -- 4.6.4 Accident Responsibility -- 4.6.5 Radar Interference -- 4.7 Advantages of Autonomous Vehicles -- 4.8 Use Cases for Autonomous Vehicle Technology -- 4.8.1 Five Use Cases -- 4.9 Future Aspects of Autonomous Vehicles -- 4.9.1 Levels of Vehicle Autonomy -- 4.9.2 Safer Mobility Technology -- 4.9.3 Industry Collaboration and Policy Matters -- 4.10 Summary -- References -- Chapter 5 Autonomous Car Driver Assistance System -- 5.1 Introduction -- 5.1.1 Traffic Video Surveillance -- 5.1.2 Need for the Research Work -- 5.2 Related Work -- 5.3 Methodology -- 5.3.1 Intelligent Driver Assistance System -- 5.3.2 Traffic Police Hand Gesture Region Identification -- 5.3.3 Vehicle Brake and Indicator Light Identification.
5.4 Results and Analysis -- 5.5 Conclusion -- References -- Chapter 6 AI-Powered Drones for Healthcare Applications -- 6.1 Introduction -- 6.1.1 Role of Artificial Intelligence in Drone Technology -- 6.1.2 Unmanned Aerial Vehicle-Drone Technology -- 6.2 Kinds of Drones Used by Medical Professionals -- 6.2.1 Multirotor -- 6.2.2 Only One Rotor -- 6.2.3 Permanent-Wing Drones -- 6.2.4 Drones for Passenger Ambulances -- 6.3 Medical and Public Health Surveillance -- 6.3.1 Telemedicine -- 6.3.2 Drones as Medical Transportation Devices -- 6.3.3 Advanced System for First Aid for the Elderly People -- 6.4 Potential Benefits of Drones in the Healthcare Industry -- 6.4.1 Top Medical Drone Delivery Services -- 6.4.2 Limitations of Drones in Healthcare -- 6.4.3 The Influence of COVID on Drones -- 6.4.4 Limitations of Drone Technology in the Healthcare Industry -- 6.4.4.1 Privacy -- 6.4.4.2 Legal Concerns -- 6.4.4.3 Rapid Transit-One of the Biggest Drawbacks of Drones is Time -- 6.4.4.4 Bugs in the Technology -- 6.4.4.5 Dependence on Weather -- 6.4.4.6 Hackable Drone Technology -- 6.5 Conclusion -- References -- Chapter 7 An Approach for Avoiding Collisions with Obstacles in Order to Enable Autonomous Cars to Travel Through Both Static and Moving Environments -- 7.1 Introduction -- 7.1.1 A Brief Overview of Driverless Cars -- 7.1.2 Objectives -- 7.1.3 Possible Uses for a Car Without a Driver -- 7.2 Related Works -- 7.3 Methodology of the Proposed Work -- 7.4 Experimental Results and Analysis -- 7.5 Results and Analysis -- 7.6 Conclusion -- References -- Chapter 8 Drivers' Emotions' Recognition Using Facial Expression from Live Video Clips in Autonomous Vehicles -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Face Detection -- 8.2.2 Facial Emotion Recognition -- 8.3 Proposed Method -- 8.3.1 Dataset -- 8.3.2 Preprocessing -- 8.3.3 Grayscale Equalization.
8.4 Results and Analysis -- 8.5 Conclusions -- References -- Chapter 9 Models for the Driver Assistance System -- 9.1 Introduction -- 9.2 Related Survey -- 9.3 Proposed Methodology -- 9.3.1 Proposed System -- 9.3.2 Data Acquisition -- 9.3.3 Noise Reduction -- 9.3.4 Feature Extraction -- 9.3.5 Histogram of Oriented Gradients -- 9.3.6 Local Binary Pattern -- 9.3.7 Feature Selection -- 9.3.8 Classification -- 9.4 Experimental Study -- 9.4.1 Quantitative Investigation on the NTHU Drowsy Driver Detection Dataset -- 9.5 Conclusion -- References -- Chapter 10 Control of Autonomous Underwater Vehicles -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Control Problem in AUV Control System -- 10.4 Methodology -- 10.5 Results -- References -- Chapter 11 Security and Privacy Issues of AI in Autonomous Vehicles -- 11.1 Introduction -- 11.2 Development of Autonomous Cars with Existing Review -- 11.3 Automation Levels of Autonomous Vehicles -- 11.4 The Architecture of an Autonomous Vehicle -- 11.5 Threat Model -- 11.6 Autonomous Vehicles with AI in IoT-Enabled Environments -- 11.7 Physical Attacks Using AI Against Autonomous Vehicles -- 11.8 AI Cybersecurity Issues for Autonomous Vehicles -- 11.9 Cyberattack Defense Mechanisms -- 11.9.1 Identity-Based Approach -- 11.9.2 Key-Based Solution -- 11.9.3 Trust-Based Solution -- 11.9.4 Solution Based on Behavior Detection -- 11.10 Solution Based on Machine Learning -- 11.11 Conclusion -- References -- Index -- EULA.
Record Nr. UNINA-9911019479003321
Rajendran Sathiyaraj  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Drones for Transportation Logistics and Disaster Management
Drones for Transportation Logistics and Disaster Management
Autore Prasanth A
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (430 pages)
Altri autori (Persone) DhanarajRajesh Kumar
SabharwalMunish
SharmaVandana
KadrySeifedine
ISBN 1-394-38645-1
1-394-38644-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Journey to Transportation and Logistics Management Using Drone: Digitization and Technological Evolution -- 1.1 Introduction -- 1.1.1 Drone Development and Advancements -- 1.1.2 Sustainability Concern -- 1.2 Literature Review -- 1.3 Fundamental Elements of Drone Technology -- 1.4 Evolution of Drone Technology -- 1.5 Use Case in Various Sectors -- 1.6 Application in Transportation and Logistics -- 1.7 Conclusion -- References -- Chapter 2 Challenges of Big Data Implementation in Drone-Based Logistics -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Big Data in Transit -- 2.4 Factors Affecting UAV Implementation in Logistics -- 2.4.1 Legal Factors -- 2.4.2 Financial Factors -- 2.4.3 Knowledge and Behavioral Factors -- 2.4.4 Privacy and Safety Factors -- 2.5 Conclusion -- References -- Chapter 3 Frameworks for Handover Management for the Networks of Future Drones -- 3.1 An Overview -- 3.2 Literature Review -- 3.3 Handover Management for Future Drone Networks (HOM-FDN) -- 3.4 Results and Discussion -- 3.4.1 Accuracy Analysis -- 3.4.2 Efficiency Analysis -- 3.4.3 Performance Analysis -- 3.4.4 Safety Analysis -- 3.4.5 Prediction Analysis -- 3.5 Conclusion -- References -- Chapter 4 Convergence of Internet of Vehicle Things and Drones: An Interoperability Perspective -- 4.1 Introduction -- 4.1.1 Overview of IoT in Vehicles and Drones -- 4.1.2 Significance of Convergence -- 4.1.3 Significance of Interoperability -- 4.2 Communication Standards for Integration -- 4.2.1 Vehicular Communication Standards -- 4.2.2 Drone Communication Standards -- 4.2.3 Cross-Domain Communication Standards -- 4.2.4 Interoperability Considerations -- 4.3 Data Exchange Protocols -- 4.3.1 IoVT Data Exchange Protocols -- 4.3.2 Drone Data Exchange Protocols.
4.3.3 Cross-Domain Data Exchange Protocols -- 4.3.4 Interoperability Considerations -- 4.4 Integration with Edge Computing -- 4.4.1 IoVT Integration with Edge Computing -- 4.4.2 Drone Integration with Edge Computing -- 4.5 Security and Privacy Measures -- 4.5.1 Security Measures -- 4.5.2 Privacy Measures -- 4.6 IoVT Regulations -- 4.6.1 Automotive Industry Standards -- 4.6.2 Compliance with Road Safety Regulations -- 4.6.3 Drone Regulations -- 4.7 Cross-Industry Collaboration -- 4.7.1 Synergies between IoVT and Drones -- 4.7.2 Industry Partnerships -- 4.8 Interoperability Challenges -- 4.8.1 Technical Challenges -- 4.8.2 Regulatory Challenges -- 4.9 Application and Future of IoVT and Drones -- 4.10 Conclusion -- References -- Chapter 5 5G Communication in Drones for Surveillance in Future Transportation Activities -- 5.1 Introduction -- 5.2 Overview of 5G Communication -- 5.2.1 Key Features of 5G Networks -- 5.3 Drones in Transportation -- 5.3.1 Surveillance and Inspection -- 5.3.2 Delivery Services -- 5.3.3 Current Challenges for the Usage of Drones in Transportation -- 5.4 Drones and 5G Integration -- 5.5 Network Architecture for Drone and 5G in Transportation -- 5.5.1 Infrastructure of 5G Network -- 5.5.2 Edge Computing and Low Latency -- 5.5.3 Drone Traffic Management System -- 5.5.4 Authentication and Security -- 5.5.5 Analytics and Network Monitoring -- 5.5.6 Ground Control Stations (GCS) -- 5.5.7 Regulatory Compliance -- 5.5.8 Integration with Other Transportation Systems -- 5.5.9 Redundancy and Scalability -- 5.6 Security and Privacy Considerations -- 5.6.1 Security Considerations -- 5.6.2 Privacy Considerations -- 5.6.3 Enhanced Security Features in 5G -- 5.7 Regulatory and Ethical Considerations -- 5.7.1 Regulatory Considerations -- 5.7.2 Ethical Considerations -- 5.8 Future Trends and Innovations -- 5.9 Conclusion -- References.
Chapter 6 Impact and Assessment of Artificial Intelligence-Enabled UAV for Real-Time Data Streaming Application -- 6.1 Introduction -- 6.2 Overview of UAVs Powered by AI -- 6.2.1 Understanding AI-Enabled UAVs: Definition and Features -- 6.2.2 Capabilities of AI-Enabled UAVs -- 6.2.3 Advantages of Using UAVs for Data Streaming -- 6.2.3.1 Quick and Effective Information Gathering -- 6.2.3.2 Improved Safety Features and Higher Cost- Effectiveness -- 6.2.3.3 Accuracy and Precision -- 6.2.3.4 Real-Time Decision Support -- 6.2.3.5 Access to Remote Regions -- 6.2.3.6 Enhanced Monitoring and Surveillance -- 6.2.3.7 Scalability and Flexibility -- 6.2.4 Most Important Technologies and Components Utilized -- 6.3 Applications of AI-Enabled UAVs in Real-Time Data Streaming -- 6.3.1 Environmental Monitoring and Conservation -- 6.3.1.1 Wildlife Conservation -- 6.3.1.2 Forestry and Land Governance -- 6.3.1.3 Marine and Coastal Surveillance -- 6.3.1.4 Environmental Research -- 6.3.2 Disaster Management and Response -- 6.3.2.1 Search and Rescue Operations -- 6.3.2.2 Damage Assessment -- 6.3.2.3 Hazard Monitoring -- 6.3.2.4 Evacuation Planning -- 6.3.3 Precision Agriculture and Crop Monitoring -- 6.3.3.1 Crop Health Assessment -- 6.3.3.2 Irrigation Management -- 6.3.3.3 Yield Forecasting -- 6.3.3.4 Soil Analysis -- 6.3.4 Infrastructure Inspection and Maintenance -- 6.3.4.1 Bridge and Building Inspections -- 6.3.4.2 Power Line and Utility Inspections -- 6.3.4.3 Monitoring Oil and Gas Facilities -- 6.3.4.4 Railway and Pipeline Inspections -- 6.3.5 Surveillance and Public Safety -- 6.4 Case Studies: Real-World Implementations -- 6.4.1 AI-Enabled UAVs for Monitoring Wildlife Populations -- 6.4.2 UAV-Based Aerial Imagery for Disaster Assessment -- 6.4.3 Precision Agriculture Using AI Algorithms and UAVs -- 6.4.4 Automated Infrastructure Inspection with UAVs.
6.4.5 UAVs for Surveillance and Emergency Response -- 6.4.6 Machine Learning and Deep Learning Techniques -- 6.4.6.1 Advancements in Machine Learning -- 6.4.6.2 Reinforcement Learning (RL) -- 6.4.6.3 Transfer Learning -- 6.4.6.4 Interpretable Models -- 6.4.6.5 Advancements in Deep Learning -- 6.4.6.6 Convolutional Neural Networks -- 6.4.6.7 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) -- 6.4.6.8 Generative Adversarial Networks (GANs) -- 6.4.7 Object Detection and Recognition -- 6.4.7.1 Single-Stage Object Detectors -- 6.4.7.2 Two-Stage Object Detectors -- 6.4.7.3 Optimal Object Detection -- 6.4.7.4 Fine-Grained Object Recognition -- 6.4.8 Image and Video Processing -- 6.4.8.1 Real-Time Image Segmentation -- 6.4.8.2 Image and Video Signature -- 6.4.8.3 Video Analysis -- 6.4.8.4 Model Generation for Creativity and Art -- 6.4.9 Sensor Fusion and Data Integration -- 6.4.9.1 Multimodal Sensor Fusion -- 6.4.9.2 Healthcare Data Integration -- 6.4.9.3 Environmental Monitoring -- 6.4.9.4 Industrial Automation -- 6.4.9.5 Smart Cities -- 6.5 Challenges and Considerations in AI-Enabled UAVs for Real- Time Data Streaming Applications -- 6.5.1 Data Privacy and Security -- 6.5.2 Regulatory Frameworks and Airspace Management -- 6.5.3 Technical Constraints and Limitations -- 6.5.4 Ethical Considerations and Societal Impact -- 6.6 Future Directions and Opportunities -- 6.6.1 Integration of AI and UAV Technologies -- 6.6.2 Collaborative Research and Development -- 6.6.3 Policy Implications and Standardization -- 6.6.4 Advancements in Hardware and Software Solutions -- 6.7 Conclusion and Future Enhancements -- References -- Chapter 7 Blockchain-Based Security and Privacy Solutions for Drones Systems -- 7.1 Introduction -- 7.1.1 Principle Functionality of the Blockchain -- 7.1.2 Foundation of Drones -- 7.1.3 Blockchain and Drones Security.
7.1.4 Related Work -- 7.2 Drones - The New Network Architecture -- 7.2.1 Components and Parameters -- 7.2.2 Drone Parameters -- 7.2.3 Drones Network Topology Architecture -- 7.3 Drones Privacy Solutions -- 7.4 Integration of Blockchain Functioning with Drones -- 7.5 Security Challenges and the Road Ahead -- 7.6 Conclusion -- References -- Chapter 8 Design and Development of Modular and Multifunctional UAV for Amphibious Landing and Surround Sense Module -- 8.1 Introduction -- 8.2 UAV Design Considerations -- 8.2.1 Design Parameters and Features -- 8.2.2 Aerodynamic Characteristics -- 8.2.3 Power Requirements -- 8.3 Development of Modular UAV -- 8.4 Surround Sense Module -- 8.5 Integration and Testing -- 8.6 Conclusion -- Acknowledgement -- References -- Chapter 9 Implementing Mission Critical Public Safety Using Communication in Drones Network -- 9.1 Introduction -- 9.2 Related Work -- 9.3 UAV - Characteristics and Strategies -- 9.3.1 What is UAV? -- 9.3.2 How Does UAV Work? -- 9.3.3 UAV Categorization -- 9.3.4 Drones for Public Security -- 9.3.5 UAV Placement Strategies -- 9.4 Communication Framework Support by UAVs -- 9.4.1 Potential Roles -- 9.5 Conclusion -- References -- Chapter 10 Assessing the Impact of Drones on Students' Engagement and Learning Outcomes -- 10.1 Introduction -- 10.1.1 Background and Context of the Study -- 10.1.2 Significance of Exploring Drone-Assisted Learning -- 10.1.3 Research Objectives -- 10.2 Literature Review -- 10.2.1 Overview of Drones in Education -- 10.2.2 Previous Research on Student Engagement and Learning Outcomes with Technology Integration -- 10.2.3 Theoretical Frameworks Supporting the Use of Drones in Education -- 10.3 Methodology -- 10.3.1 Research Design and Approach -- 10.3.2 Participant Selection and Sampling Strategy -- 10.3.3 Data Collection Methods -- 10.3.4 Data Analysis Plan.
10.4 Implementation of Drone-Assisted Learning Activities.
Record Nr. UNINA-9911038525403321
Prasanth A  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui