Autonomous Vehicles, Volume 1: Using Machine Intelligence

AUTONOMOUS VEHICLES Addressing the current challenges, approaches and applications relating to autonomous vehicles, this groundbreaking new volume presents the research and techniques in this growing area, using Internet of Things (IoT), Machine Learning (ML), Deep Learning, and Artificial Intelligence (AI). This book provides and addresses the current challenges, approaches, and applications relating to autonomous vehicles, using Internet of Things (IoT), machine learning, deep learning, and Artificial Intelligence (AI) techniques. Several self-driving or autonomous (“driverless”) cars, trucks, and drones incorporate a variety of IoT devices and sensing technologies such as sensors, gyroscopes, cloud computing, and fog layer, allowing the vehicles to sense, process, and maintain massive amounts of data on traffic, routes, suitable times to travel, potholes, sharp turns, and robots for pipe inspection in the construction and mining industries. Few books are available on the practical applications of unmanned aerial vehicles (UAVs) and autonomous vehicles from a multidisciplinary approach. Further, the available books only cover a few applications and designs in a very limited scope. This new, groundbreaking volume covers real-life applications, business modeling, issues, and solutions that the engineer or industry professional faces every day that can be transformed using intelligent systems design of autonomous systems. Whether for the student, veteran engineer, or another industry professional, this book, and its companion volume, are must-haves for any library. Cover Title Page Copyright Page Contents Preface Chapter 1 Anomalous Activity Detection Using Deep Learning Techniques in Autonomous Vehicles 1.1 Introduction 1.1.1 Organization of Chapter 1.2 Literature Review 1.3 Artificial Intelligence in Autonomous Vehicles 1.4 Technologies Inside Autonomous Vehicle 1.5 Major Tasks in Autonomous Vehicle Using AI 1.6 Benefits of Autonomous Vehicle 1.7 Applications of Autonomous Vehicle 1.8 Anomalous Activities and Their Categorization 1.9 Deep Learning Methods in Autonomous Vehicle 1.10 Working of Yolo 1.11 Proposed Methodology 1.12 Proposed Algorithms 1.13 Comparative Study and Discussion 1.14 Conclusion References Chapter 2 Algorithms and Difficulties for Autonomous Cars Based on Artificial Intelligence 2.1 Introduction 2.1.1 Algorithms for Machine Learning in Autonomous Driving 2.1.2 Regression Algorithms 2.1.3 Design Identification Systems (Classification) 2.1.4 Grouping Concept 2.1.5 Decision Matrix Algorithms 2.2 In Autonomous Cars, AI Algorithms are Applied 2.2.1 Algorithms for Route Planning and Control 2.2.2 Method for Detecting Items 2.2.3 Algorithmic Decision-Making 2.3 AI’s Challenges with Self-Driving Vehicles 2.3.1 Feedback in Real Time 2.3.2 Complexity of Computation 2.3.3 Black Box Behavior 2.3.4 Precision and Dependability 2.3.5 The Safeguarding 2.3.6 AI and Security 2.3.7 AI and Ethics 2.4 Conclusion References Chapter 3 Trusted Multipath Routing for Internet of Vehicles against DDoS Assault Using Brink Controller in Road Awareness (TMRBC-IOV) 3.1 Introduction 3.2 Related Work 3.3 VANET Grouping Algorithm (VGA) 3.4 Extension of Trusted Multipath Distance Vector Routing (TMDR-Ext) 3.5 Conclusion References Chapter 4 Technological Transformation of Middleware and Heuristic Approaches for Intelligent Transport System 4.1 Introduction 4.2 Evolution of VANET 4.3 Middleware Approach 4.4 Heuristic Search 4.5 Reviews of Middleware Approaches 4.6 Reviews of Heuristic Approaches 4.7 Conclusion and Future Scope References Chapter 5 Recent Advancements and Research Challenges in Design and Implementation of Autonomous Vehicles 5.1 Introduction 5.1.1 History and Motivation 5.1.2 Present Scenario and Need for Autonomous Vehicles 5.1.3 Features of Autonomous Vehicles 5.1.4 Challenges Faced by Autonomous Vehicles 5.2 Modules/Major Components of Autonomous Vehicles 5.2.1 Levels of Autonomous Vehicles 5.2.2 Functional Components of An Autonomous Vehicle 5.2.3 Traffic Control System of Autonomous Vehicles 5.2.4 Safety Features Followed by Autonomous Vehicles 5.3 Testing and Analysis of An Autonomous Vehicle in a Virtual Prototyping Environment 5.4 Application Areas of Autonomous Vehicles 5.5 Artificial Intelligence (AI) Approaches for Autonomous Vehicles 5.5.1 Pedestrian Detection Algorithm (PDA) 5.5.2 Road Signs and Traffic Signal Detection 5.5.3 Lane Detection System 5.6 Challenges to Design Autonomous Vehicles 5.7 Conclusion References Chapter 6 Review on Security Vulnerabilities and Defense Mechanism in Drone Technology 6.1 Introduction 6.2 Background 6.3 Security Threats in Drones 6.3.1 Electronics Attacks 6.3.1.1 GPS and Communication Jamming Attacks 6.3.1.2 GPS and Communication Spoofing Attacks 6.3.1.3 Eavesdropping 6.3.1.4 Electromagnetic Interference 6.3.1.5 Laser Attacks 6.3.2 Cyber-Attacks 6.3.2.1 Man-in-Middle Attacks 6.3.2.2 Black Hole and Grey Hole 6.3.2.3 False Node Injection 6.3.2.4 False Communication Data Injection 6.3.2.5 Firmware’s Manipulations 6.3.2.6 Sleep Deprivation 6.3.2.7 Malware Infection 6.3.2.8 Packet Sniffing 6.3.2.9 False Database Injection 6.3.2.10 Replay Attack 6.3.2.11 Network Isolations 6.3.2.12 Code Injection 6.3.3 Physical Attacks 6.3.3.1 Key Logger Attacks 6.3.3.2 Camera Spoofing 6.4 Defense Mechanism and Countermeasure Against Attacks 6.4.1 Defense Techniques for GPS Spoofing 6.4.2 Defense Technique for Man-in-Middle Attacks 6.4.3 Defense against Keylogger Attacks 6.4.4 Defense against Camera Spoofing Attacks 6.4.5 Defense against Buffer Overflow Attacks 6.4.6 Defense against Jamming Attack 6.5 Conclusion References Chapter 7 Review of IoT-Based Smart City and Smart Homes Security Standards in Smart Cities and Home Automation 7.1 Introduction 7.2 Overview and Motivation 7.3 Existing Research Work 7.4 Different Security Threats Identified in IoT-Used Smart Cities and Smart Homes 7.4.1 Security Threats at Sensor Layer 7.4.1.1 Eavesdropping Attacks 7.4.1.2 Node Capturing Attacks 7.4.1.3 Sleep Deprivation Attacks 7.4.1.4 Malicious Code Injection Attacks 7.4.2 Security Threats at Network Layer 7.4.2.1 Distributed Denial of Service (DDOS) Attack 7.4.2.2 Sniffing Attack 7.4.2.3 Routing Attack 7.4.2.4 Traffic Examination Attacks 7.4.3 Security Threats at Platform Layer 7.4.3.1 SQL Injection 7.4.3.2 Cloud Malware Injection 7.4.3.3 Storage Attacks 7.4.3.4 Side Channel Attacks 7.4.4 Security Threats at Application Layer 7.4.4.1 Sniffing Attack 7.4.4.2 Reprogram Attack 7.4.4.3 Data Theft 7.4.4.4 Malicious Script Attack 7.5 Security Solutions For IoT-Based Environment in Smart Cities and Smart Homes 7.5.1 Blockchain 7.5.2 Lightweight Cryptography 7.5.3 Biometrics 7.5.4 Machine Learning 7.6 Conclusion References Chapter 8 Traffic Management for Smart City Using Deep Learning 8.1 Introduction 8.2 Literature Review 8.3 Proposed Method 8.4 Experimental Evaluation 8.4.1 Hardware and Software Configuration 8.4.2 About Dataset 8.4.3 Implementation 8.4.4 Result 8.5 Conclusion References Chapter 9 Cyber Security and Threat Analysis in Autonomous Vehicles 9.1 Introduction 9.2 Autonomous Vehicles 9.2.1 Autonomous vs. Automated 9.2.2 Significance of Autonomous Vehicles 9.2.3 Challenges in Autonomous Vehicles 9.2.4 Future Aspects 9.3 Related Works 9.4 Security Problems in Autonomous Vehicles 9.4.1 Different Attack Surfaces and Resulting Attacks 9.5 Possible Attacks in Autonomous Vehicles 9.5.1 Internal Network Attacks 9.5.2 External Attacks 9.6 Defence Strategies against Autonomous Vehicle Attacks 9.6.1 Against Internal Network Attacks 9.6.2 Against External Attack 9.7 Cyber Threat Analysis 9.8 Security and Safety Standards in AVs 9.9 Conclusion References Chapter 10 Big Data Technologies in UAV’s Traffic Management System: Importance, Benefits, Challenges and Applications 10.1 Introduction 10.2 Literature Review 10.3 Overview of UAV’s Traffic Management System 10.4 Importance of Big Data Technologies and Algorithm 10.5 Benefits of Big Data Techniques in UTM 10.6 Challenges of Big Data Techniques in UTM 10.7 Applications of Big Data Techniques in UTM 10.8 Case Study and Future Aspects 10.9 Conclusion References Chapter 11 Reliable Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles 11.1 Introduction 11.2 Literature Survey 11.3 Proposed Architecture 11.4 Experimental Results 11.5 Analysis of the Proposal 11.6 Conclusion References Chapter 12 Multitask Learning for Security and Privacy in IoV (Internet of Vehicles) 12.1 Introduction 12.2 IoT Architecture 12.3 Taxonomy of Various Security Attacks in Internet of Things 12.3.1 Perception Layer Attacks 12.3.2 Network Layer Attacks 12.3.3 Application Layer Attacks 12.4 Machine Learning Algorithms for Security and Privacy in IoV 12.5 A Machine Learning-Based Learning Analytics Methodology for Security and Privacy in Internet of Vehicles 12.5.1 Methodology 12.5.2 Result Analysis 12.6 Conclusion References Chapter 13 ML Techniques for Attack and Anomaly Detection in Internet of Things Networks 13.1 Introduction 13.2 Internet of Things 13.3 Cyber-Attack in IoT 13.4 IoT Attack Detection in ML Technics 13.5 Conclusion References Chapter 14 Applying Nature-Inspired Algorithms for Threat Modeling in Autonomous Vehicles 14.1 Introduction 14.2 Related Work 14.3 Proposed Mechanism 14.4 Performance Results 14.5 Future Directions 14.6 Conclusion References Chapter 15 The Smart City Based on AI and Infrastructure: A New Mobility Concepts and Realities 15.1 Introduction 15.2 Research Method 15.3 Vehicles that are Both Networked and Autonomous 15.4 Personal Aerial Automobile Vehicles and Unmanned Aerial Automobile Vehicles 15.5 Mobile Connectivity as a Service 15.6 Major Role for Smart City Development with IoT and Industry 4.0 15.7 Conclusion References Index EULA