Autonomous Mobility AI Engineer (AMAE)

Length: 2 Days

This course equips participants with the essential skills and knowledge needed to excel in the field of autonomous mobility AI engineering. Participants will learn the intricacies of developing and integrating AI technologies into autonomous vehicles and advanced mobility solutions, preparing them for a career at the forefront of innovation in transportation.

Learning Objectives:

• Understand the fundamentals of autonomous mobility and AI technologies.
• Gain proficiency in designing and implementing AI algorithms for autonomous vehicle control.
• Learn to integrate sensor systems and perception algorithms for environment perception.
• Explore techniques for data processing, machine learning, and deep learning in autonomous mobility.
• Develop skills in simulation and testing methodologies for autonomous systems.
• Master the principles of safety, security, and regulatory compliance in autonomous mobility AI engineering.

Audience: This course is designed for engineers, developers, and professionals aspiring to specialize in autonomous mobility AI engineering. It is suitable for individuals with a background in computer science, electrical engineering, mechanical engineering, or related fields seeking to advance their careers in the autonomous vehicle industry.

Course Outline:

Module 1: Introduction to Autonomous Mobility and AI Technologies

• Evolution of Autonomous Mobility
• Basics of Artificial Intelligence
• Autonomous Vehicle Architecture
• Role of AI in Mobility Solutions
• Industry Trends and Challenges
• Ethical Considerations in Autonomous Mobility

Module 2: AI Algorithms for Autonomous Vehicle Control

• Path Planning Algorithms
• Decision-Making Algorithms
• Control Systems for Autonomous Vehicles
• Reinforcement Learning Techniques
• Optimization Algorithms
• Real-Time Implementation Challenges

Module 3: Sensor Systems and Perception Algorithms

• Types of Sensors Used in Autonomous Vehicles
• Sensor Fusion Techniques
• Object Detection and Tracking Algorithms
• Localization and Mapping Techniques
• Perception in Challenging Environments
• Integration of Sensor Data with AI Systems

Module 4: Data Processing, Machine Learning, and Deep Learning in Autonomous Mobility

• Preprocessing of Sensor Data
• Supervised and Unsupervised Learning Methods
• Neural Networks for Autonomous Systems
• Deep Reinforcement Learning
• Transfer Learning for Autonomous Vehicles
• Handling Big Data in Autonomous Mobility

Module 5: Simulation and Testing Methodologies for Autonomous Systems

• Importance of Simulation in Autonomous Vehicle Development
• Simulation Tools and Platforms
• Scenario-based Testing
• Validation and Verification Techniques
• Hardware-in-the-Loop (HIL) Simulation
• Simulation for Safety-Critical Scenarios

Module 6: Safety, Security, and Regulatory Compliance in Autonomous Mobility AI Engineering

• Safety Standards for Autonomous Vehicles
• Functional Safety in AI Systems
• Cybersecurity Challenges in Autonomous Mobility
• Regulatory Frameworks and Compliance Requirements
• Risk Assessment and Mitigation Strategies
• Future Directions in Safety and Security for Autonomous Mobility

Exam Domains:

1. Machine Learning Fundamentals
   • Concepts of supervised, unsupervised, and reinforcement learning
   • Model evaluation and validation techniques
   • Feature engineering and selection
2. Deep Learning and Neural Networks
   • Basics of neural network architecture
   • Convolutional neural networks (CNNs) for computer vision tasks
   • Recurrent neural networks (RNNs) for sequential data analysis
   • Advanced architectures like Transformers and GANs
3. Computer Vision for Autonomous Mobility
   • Object detection and recognition
   • Semantic segmentation and instance segmentation
   • Depth estimation and visual odometry
4. Sensor Fusion and Localization
   • Integration of data from multiple sensors (LiDAR, radar, cameras, IMUs)
   • Localization techniques such as SLAM (Simultaneous Localization and Mapping)
5. Planning and Control Algorithms
   • Path planning algorithms for autonomous navigation
   • Motion planning in dynamic environments
   • PID control, MPC (Model Predictive Control), and other control strategies
6. Simulation and Testing
   • Simulation environments for autonomous vehicles
   • Testing methodologies including virtual testing, simulation-based validation, and real-world testing
7. Ethics and Safety in Autonomous Systems
   • Ethical considerations in autonomous mobility
   • Safety standards and regulations for autonomous vehicles
   • Risk assessment and mitigation strategies

Question Types:

1. Multiple Choice Questions (MCQs): Assessing theoretical knowledge across all domains.
2. Code Implementation and Debugging: Hands-on coding tasks to implement algorithms or fix errors in provided code snippets.
3. Scenario-based Questions: Presenting real-world scenarios and asking candidates to propose solutions or identify potential issues.
4. Short Answer Questions: Concise explanations of concepts, algorithms, or methodologies.
5. Case Studies: Analyzing case studies related to autonomous mobility systems and answering questions based on them.

Passing Criteria:

To pass the Autonomous Mobility AI Engineer (AMAE) Training exam, candidates must:

• Achieve a minimum score of 70% across all domains.
• Score at least 60% in each individual domain.