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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring the levels of autonomous driving and AI integration
- Survey of key AI frameworks and libraries utilized in the field
- Emerging trends and innovations in AI-driven vehicle autonomy
Deep Learning Foundations for Autonomous Driving
- Neural network architectures tailored for self-driving applications
- Convolutional Neural Networks (CNNs) for image analysis
- Recurrent Neural Networks (RNNs) for processing temporal data
Computer Vision in Autonomous Driving
- Object detection utilizing YOLO and SSD architectures
- Techniques for lane detection and road following
- Semantic segmentation for environmental awareness
Reinforcement Learning for Driving Decisions
- Markov Decision Processes (MDP) in the context of autonomous vehicles
- Training Deep Reinforcement Learning (DRL) models
- Simulation-based learning to refine driving policies
Sensor Fusion and Perception
- Combining data from LiDAR, RADAR, and cameras
- Application of Kalman filtering and sensor fusion methods
- Multi-sensor data processing for environmental mapping
Deep Learning Models for Driving Prediction
- Constructing behavioral prediction models
- Forecasting trajectories to avoid obstacles
- Recognizing driver state and intent
Model Evaluation and Optimization
- Key metrics for assessing model accuracy and performance
- Strategies for optimizing models for real-time execution
- Deploying trained models onto autonomous vehicle platforms
Case Studies and Real-World Applications
- Reviewing autonomous vehicle incidents and safety considerations
- Examining successful deployments of AI-driven driving systems
- Capstone Project: Building a lane-following AI model
Requirements
- Strong proficiency in Python programming
- Practical experience with machine learning and deep learning frameworks
- Working knowledge of automotive technology and computer vision principles
Target Audience
- Data scientists seeking to specialize in autonomous driving applications
- AI specialists focused on advancing automotive AI development
- Developers aiming to apply deep learning techniques to self-driving vehicle technologies
21 Hours
