Explainable Artificial Intelligence for Autonomous Vehicles: Concepts, Challenges, and Applications (Explainable AI (XAI) for Engineering Applications)

✍ Scribed by Kamal Malik (editor), Moolchand Sharma (editor), Suman Deswal (editor), Umesh Gupta (editor), Deevyankar Agarwal (editor), Yahya Obaid Bakheet Al Shamsi (editor)

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 205
• Edition: 1
• Category: Library

✦ Synopsis

Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance.

This book:

• Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems.
• Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles.
• Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making.
• Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control.
• Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles.

The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.

✦ Table of Contents

Cover
Half Title
Series
Title
Copyright
Dedication
Contents
Preface
About the editors
List of contributors
1 Autonomous vehicles
1.1 Introduction
1.2 Importance of artificial intelligence (AI) in autonomous vehicles
1.3 AI-driven decision making
1.4 AI techniques and deep learning algorithms
1.5 Sensor fusion and data integration in autonomous vehicles
1.6 Perception system in autonomous vehicles
1.7 Human-AI interaction in autonomous vehicles
1.8 Safety and reliability in AI-driven autonomous vehicles
1.9 Conclusion
References
2 Explainable artificial intelligence: fundamentals, approaches, challenges, XAI evaluation, and validation
2.1 Fundamentals of XAI
2.2 Introduction to explainable artificial intelligence
2.3 XAI and its significance
2.4 Key concepts in explainability
2.4.1 Model transparency
2.4.2 Interpretability vs. transparency
2.4.3 Trustworthiness
2.5 Approaches to developing XAI models
2.6 Model transparency
2.6.1 Transparent models in XAI
2.6.2 Limitations and use cases
2.7 Rule-based systems
2.7.1 Rule-based approaches to XAI
2.7.2 Scalability and complexity
2.8 Feature importance analysis
2.8.1 Shap and lime methods
2.8.2 Applications in various domains
2.9 Visualization techniques
2.9.1 Visualizing model decisions
2.9.2 Practical implementations
2.10 Challenges of implementing XAI in autonomous vehicles
2.11 Trade-Offs between performance and explainability
2.11.1 Balancing act: performance vs. interpretability
2.11.2 Strategies for achieving balance
2.12 Handling uncertainty
2.12.1 Uncertainty in autonomous vehicle context
2.12.2 Probabilistic models and uncertainty management
2.13 Safety and reliability
2.13.1 Safety considerations in XAI
2.13.2 Integration of safety mechanisms
2.14 Human-AI interaction
2.14.1 Designing user-friendly XAI interfaces
2.14.2 Ensuring positive user experience
2.15 XAI evaluation and validation
2.16 Metrics for evaluating explainability
2.16.1 Measuring fidelity, comprehensibility, and trustworthiness
2.16.2 Tailoring metrics to specific use cases
2.17 User studies
2.17.1 Conducting user-centric XAI evaluations
2.17.2 Methodologies and best practices
2.18 Simulation and testing
2.18.1 Simulated environments for XAI validation
2.18.2 Real-world testing scenarios
2.19 Regulatory compliance
2.19.1 Regulatory frameworks for XAI integration
2.19.2 Industry standards and guidelines
2.20 Conclusion
References
3 Explainable artificial intelligence in autonomous vehicles: prospects and future direction
3.1 Introduction
3.2 Current state of XAI in autonomous vehicles
3.2.1 Autonomous vehicles
3.2.2 Explainable artificial intelligence (XAI)
3.2.3 Case studies of XAI techniques in autonomous vehicles
3.3 Challenges and limitations of XAI in autonomous vehicles
3.4 Future trends in XAI for autonomous vehicles
3.5 Conclusion
References
4 XAI applications in autonomous vehicles
4.1 Introduction
4.2 Background and review of related work
4.2.1 XAI method for convolutional neural networks in self-driving cars
4.2.2 The internet of vehicles structure and need for XAI-IDS
4.2.3 XAI frameworks
4.2.4 Practical implementation of XAI-based models
4.3 Internet of vehicles (IoV) network architecture
4.3.1 Autonomous vehicle components and design
4.3.2 Applications and services
4.3.3 Current issues
4.4 XAI methods and algorithms
4.4.1 XAI methods can be sub-divided into four categories
4.4.2 XAI algorithms in autonomous vehicles
4.5 XAI models to improve overall system performance
4.6 Discussion
4.7 Conclusion
References
5 Emerging applications and future scope of internet of vehicles for smart cities: a survey
5.1 Introduction
5.2 Layered architecture of IoV
5.3 Literature survey
5.3.1 Applications of IoV in smart cities
5.4 Issues and challenges of IoV
5.5 Future scope of IoV
5.6 Conclusion
References
6 Future issues and challenges of internet of vehicles: a survey
6.1 Introduction
6.2 Literature survey
6.3 IoV ecosystem
6.4 Internet of vehicles applications
6.5 Summarized challenges and future research directions
6.6 Conclusion
References
7 Feature designing and security considerations in electrical vehicles utilizing explainable AI
7.1 Feature designing for smart electrical vehicles
7.2 Explainable recommendations and decision support
7.2.1 Building trust through explainable recommendations
7.3 Addressing user concerns and misconceptions
7.3.1 User education and training
7.3.2 Continuous improvement and feedback
7.3.3 User feedback and iterative design
7.3.4 Importance of user feedback
7.3.5 Gathering user feedback
7.3.6 Surveys and questionnaires
7.3.7 User interviews and focus groups
7.3.8 User testing and observations
7.4 Online communities and social media
7.4.1 Incorporating explainable AI in user feedback
7.4.2 Safety considerations in smart cars
7.4.3 Importance of safety in smart cars
7.4.4 Safety challenges in smart cars
7.4.5 Explainable AI for safety in smart cars
7.4.6 Decision explanation
7.4.7 Error detection and diagnosis
7.4.8 Safety validation and certification
7.4.9 Privacy and data protection
7.4.10 Collaborative safety
7.4.11 Human-machine interaction for safety
7.4.12 Security challenges in smart cars
7.4.13 Cybersecurity risks
7.4.14 Data privacy and protection
7.4.15 Malicious attacks on AI systems
7.4.16 Supply chain security
7.4.17 Over-the-air updates
7.4.18 XAI for security enhancement
7.4.19 Explainable AI for safety and security
7.4.20 Enhancing safety with explainable AI
7.4.21 Real-time risk assessment
7.4.22 Error detection and diagnosis
7.4.23 Safety-critical decision support
7.4.24 Strengthening security with explainable AI
7.4.25 Intrusion detection and prevention
7.4.26 Vulnerability assessment
7.4.27 Adversarial attack detection
7.4.28 Regulatory compliance and accountability
7.4.29 Compliance with safety standards
7.4.30 Ethical decision-making
7.4.31 Accountability and liability
7.4.32 Importance of privacy and data protection in smart cars
7.4.33 Privacy challenges in smart cars
7.4.34 Role of explainable AI in privacy and data protection
7.4.35 Challenges in implementing XAI for privacy and data protection
References
8 Feature detection and feature visualization in smart cars utilizing explainable AI
8.1 Introduction
8.1.1 Feature visualization
8.1.2 Benefits of feature importance and feature visualization
8.1.3 Challenges and limitations
8.1.4 Local explanations and counterfactuals
8.1.5 Local explanations
8.1.6 Counterfactuals
8.1.7 Benefits and applications
8.1.8 Model-agnostic explanations
8.1.9 Understanding model-agnostic explanations
8.1.10 Techniques for model-agnostic explanations
8.1.11 Global explanations
8.1.12 Local explanations
8.1.13 Application of model-agnostic explanations in smart cars
8.1.14 Safety and decision-making
8.1.15 Regulatory compliance and accountability
8.1.16 User experience and trust
8.1.17 Rule extraction and rule sets
8.1.18 Rule extraction techniques
8.1.19 Rule sets for decision-making
8.1.20 Benefits and limitations of rule extraction and rule sets
References
Index