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Submit a ProposalAI for Autonomous Vehicles
 | The Future of Driverless Technology Edited by Sathiyaraj Rajendran, Munish Sabharwal, Yu-Chen Hu, Rajesh Kumar Dhanaraj, and Balamurugan Balusamy Copyright: 2024 | Status: Published ISBN: 9781119847465 | Hardcover | 262 pages |
One Line Description
This title presents the research prospects and security challenges present in the exciting new field of autonomous vehicles by comprehensively introducing advanced concepts of artificial intelligence (AI) in driverless technology.
Audience
Researchers and industrial professionals working in AI for autonomous vehicles, AI data analysts, AI engineers, robotics scientists, marketing, E-commerce, data scientists, and students
Researchers and industrial professionals working in AI for autonomous vehicles, AI data analysts, AI engineers, robotics scientists, marketing, E-commerce, data scientists, and students
Description
With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society. The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles.
There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology.
The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.
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With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society. The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles.
There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology.
The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.
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Author / Editor Details
Sathiyaraj Rajendran, PhD is an assistant professor in the School of Engineering & Technology at the Chikka Muniyappa Reddy University, Bangalore. He completed his PhD at Anna University, Chennai. He has more than nine years of experience and has collaborated actively with researchers in several other disciplines of computer science, particularly traffic prediction systems and intelligent systems. Additionally, he has authored more than 25 publications and filed five patents.
Sathiyaraj Rajendran, PhD is an assistant professor in the School of Engineering & Technology at the Chikka Muniyappa Reddy University, Bangalore. He completed his PhD at Anna University, Chennai. He has more than nine years of experience and has collaborated actively with researchers in several other disciplines of computer science, particularly traffic prediction systems and intelligent systems. Additionally, he has authored more than 25 publications and filed five patents.
Munish Sabharwal, PhD is a professor and dean in the School of Computing Science & Engineering, Galgotias University, Greater Noida, India, as well as an adjunct professor in the department of Applied Mathematics and IT at Samarkand State University, Samarkand, Uzbekistan. He has contributed over 21 years in teaching, education management, research, and software development. Additionally, he has published more than 55 research papers in conferences and journals and three books.
Yu-Chen Hu, PhD is a professor in the Department of Computer Science and Information Management, Providence University, Sha-Lu, Taiwan. He is a senior member of Institute of Electrical and Electronics Engineers. He is also a member of Computer Vision, Graphics, and Image Processing (CVGIP), the Chinese Cryptology and Information Security Association (CCISA), Computer Science and Information Management (CSIM), and the Phi Tau Phi Society of the Republic of China. His research interests include digital forensics, information hiding, image and signal processing, data compression, information security, and data engineering.
Rajesh Kumar Dhanaraj, PhD is an associate professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, Uttar Pradesh, India. He has published over 35 articles in various journals and conference proceedings and contributed chapters to many books. In addition to his teaching role, he is also an Expert Advisory Panel Member of Texas Instruments Inc., USA.
Balamurugan Balusamy is a professor at Galgotias University, Greater Noida, Uttar Pradesh, India with over 14 years of experience. He has published close to 30 books on various technologies, as well as over 150 quality journal, conference, and book chapters combined, visiting over 15 countries for his technical discourse. He serves on the advisory committee for several startups and forums and does consultancy work for the industry on Industrial Internet of Things.
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Table of Contents
Preface
1. Artificial Intelligence in Autonomous Vehicles—A Survey of Trends and Challenges
Umamaheswari Rajasekaran, A. Malini and Mahalakshmi Murugan
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
2. Age of Computational AI for Autonomous Vehicles
Akash Mohanty, U. Rahamathunnisa, K. Sudhakar and R. Sathiyaraj
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
3. State of the Art of Artificial Intelligence Approaches Toward Driverless Technology
Sriram G. K., A. Malini and Santhosh K.M.R.
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
4. A Survey on Architecture of Autonomous Vehicles
Ramyavarshini P., A. Malini and Mahalakshmi S.
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
5. Autonomous Car Driver Assistance System
R. Annamalai, S. Sudha Mercy, J. M. Mathana, N. Banupriya, Rajalakshmi S. and S. D. Lalitha
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
6. AI-Powered Drones for Healthcare Applications
M. Nalini
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
7. An Approach for Avoiding Collisions with Obstacles in Order to Enable Autonomous Cars to Travel Through Both Static and Moving Environments
T. Sivadharshan, K. Kalaivani, N. Golden Stepha, Rajitha Jasmine R., A. Jasmine Gilda and S. Godfrey
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
8. Drivers’ Emotions’ Recognition Using Facial Expression from Live Video Clips in Autonomous Vehicles
Tumaati Rameshtrh, Anusha Sanampudi, S. Srijayanthis, S. Vijayakumarsvk, Vijayabhaskar and S. Gomathigomathi
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
9. Models for the Driver Assistance System
B. Shanthini, K. Cornelius, M. Charumathy, Lekshmy P., P. Kavitha and T. Sethukarasi
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
10. Control of Autonomous Underwater Vehicles
M. P. Karthikeyan, S. Anitha Jebamani, P. Umaeswari, K. Chitti Babu, C. Geetha and Kirupavathi S.
10.1 Introduction
10.2 Literature Review
10.3 Control Problem in AUV Control System
10.4 Methodology
10.5 Results
References
11. Security and Privacy Issues of AI in Autonomous Vehicles
K. Ramalakshmi, Sankar Ganesh and L. KrishnaKumari
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
Back to Top
Preface
1. Artificial Intelligence in Autonomous Vehicles—A Survey of Trends and Challenges
Umamaheswari Rajasekaran, A. Malini and Mahalakshmi Murugan
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
2. Age of Computational AI for Autonomous Vehicles
Akash Mohanty, U. Rahamathunnisa, K. Sudhakar and R. Sathiyaraj
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
3. State of the Art of Artificial Intelligence Approaches Toward Driverless Technology
Sriram G. K., A. Malini and Santhosh K.M.R.
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
4. A Survey on Architecture of Autonomous Vehicles
Ramyavarshini P., A. Malini and Mahalakshmi S.
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
5. Autonomous Car Driver Assistance System
R. Annamalai, S. Sudha Mercy, J. M. Mathana, N. Banupriya, Rajalakshmi S. and S. D. Lalitha
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
6. AI-Powered Drones for Healthcare Applications
M. Nalini
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
7. An Approach for Avoiding Collisions with Obstacles in Order to Enable Autonomous Cars to Travel Through Both Static and Moving Environments
T. Sivadharshan, K. Kalaivani, N. Golden Stepha, Rajitha Jasmine R., A. Jasmine Gilda and S. Godfrey
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
8. Drivers’ Emotions’ Recognition Using Facial Expression from Live Video Clips in Autonomous Vehicles
Tumaati Rameshtrh, Anusha Sanampudi, S. Srijayanthis, S. Vijayakumarsvk, Vijayabhaskar and S. Gomathigomathi
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
9. Models for the Driver Assistance System
B. Shanthini, K. Cornelius, M. Charumathy, Lekshmy P., P. Kavitha and T. Sethukarasi
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
10. Control of Autonomous Underwater Vehicles
M. P. Karthikeyan, S. Anitha Jebamani, P. Umaeswari, K. Chitti Babu, C. Geetha and Kirupavathi S.
10.1 Introduction
10.2 Literature Review
10.3 Control Problem in AUV Control System
10.4 Methodology
10.5 Results
References
11. Security and Privacy Issues of AI in Autonomous Vehicles
K. Ramalakshmi, Sankar Ganesh and L. KrishnaKumari
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
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