Edge AI in Autonomous Systems

• 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

Edge AI in Autonomous Systems focuses on the application of Edge AI technologies in autonomous vehicles, drones, and robotics. This course covers real-time processing, control systems, and practical deployment of AI solutions in autonomous systems. Participants will gain hands-on experience and advanced knowledge necessary to develop and implement Edge AI in various autonomous applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.

By the end of this training, participants will be able to:

• Understand the role and benefits of Edge AI in autonomous systems.
• Develop and deploy AI models for real-time processing on edge devices.
• Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
• Design and optimize control systems using Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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