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Course Outline
Introduction to Edge AI in Autonomous Systems
- Overview of Edge AI and its significance in autonomous systems
- Key benefits and challenges of implementing Edge AI in autonomous systems
- Current trends and innovations in Edge AI for autonomy
- Real-world applications and case studies
Real-Time Processing in Autonomous Systems
- Fundamentals of real-time data processing
- AI models for real-time decision making
- Handling data streams and sensor fusion
- Practical examples and case studies
Edge AI in Autonomous Vehicles
- AI models for vehicle perception and control
- Developing and deploying AI solutions for real-time navigation
- Integrating Edge AI with vehicle control systems
- Case studies of Edge AI in autonomous vehicles
Edge AI in Drones
- AI models for drone perception and flight control
- Real-time data processing and decision making in drones
- Implementing Edge AI for autonomous flight and obstacle avoidance
- Practical examples and case studies
Edge AI in Robotics
- AI models for robotic perception and manipulation
- Real-time processing and control in robotic systems
- Integrating Edge AI with robotic control architectures
- Case studies of Edge AI in robotics
Developing AI Models for Autonomous Applications
- Overview of relevant machine learning and deep learning models
- Training and optimizing models for edge deployment
- Tools and frameworks for autonomous Edge AI (TensorFlow Lite, ROS, etc.)
- Model validation and evaluation in autonomous settings
Deploying Edge AI Solutions in Autonomous Systems
- Steps for deploying AI models on various edge hardware
- Real-time data processing and inference on edge devices
- Monitoring and managing deployed AI models
- Practical deployment examples and case studies
Ethical and Regulatory Considerations
- Ensuring safety and reliability in autonomous AI systems
- Addressing bias and fairness in autonomous AI models
- Compliance with regulations and standards in autonomous systems
- Best practices for responsible AI deployment in autonomous systems
Performance Evaluation and Optimization
- Techniques for evaluating model performance in autonomous systems
- Tools for real-time monitoring and debugging
- Strategies for optimizing AI model performance in autonomous applications
- Addressing latency, reliability, and scalability challenges
Innovative Use Cases and Applications
- Advanced applications of Edge AI in autonomous systems
- In-depth case studies in various autonomous domains
- Success stories and lessons learned
- Future trends and opportunities in Edge AI for autonomy
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application for an autonomous system
- Real-world projects and scenarios
- Collaborative group exercises
- Project presentations and feedback
Summary and Next Steps
Requirements
- An understanding of AI and machine learning concepts
- Experience with programming languages (Python recommended)
- Familiarity with robotics, autonomous systems, or related technologies
Audience
- Robotics engineers
- Autonomous vehicle developers
- AI researchers
14 Hours
