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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring levels of autonomous driving and the role of AI integration.
- Review of AI frameworks and libraries commonly used in the field.
- Examining current trends and innovations in AI-driven vehicle autonomy.
Deep Learning Fundamentals for Autonomous Driving
- Designing neural network architectures suited for self-driving cars.
- Using Convolutional Neural Networks (CNNs) for image processing tasks.
- Leveraging Recurrent Neural Networks (RNNs) to handle temporal data.
Computer Vision for Autonomous Driving
- Detecting objects using YOLO and SSD architectures.
- Techniques for lane detection and maintaining road following.
- Applying semantic segmentation to perceive the surrounding environment.
Reinforcement Learning for Driving Decisions
- Understanding Markov Decision Processes (MDP) in autonomous contexts.
- Training Deep Reinforcement Learning (DRL) models for driving tasks.
- Employing simulation-based learning to develop driving policies.
Sensor Fusion and Perception
- Integrating data from LiDAR, RADAR, and camera systems.
- Applying Kalman filtering and advanced sensor fusion techniques.
- Processing multi-sensor data for accurate environmental mapping.
Deep Learning Models for Driving Prediction
- Creating behavioral prediction models for other agents.
- Forecasting trajectories to facilitate obstacle avoidance.
- Recognizing driver state and intent using AI models.
Model Evaluation and Optimization
- Assessing model accuracy and performance using key metrics.
- Optimizing models for efficient real-time execution.
- Deploying trained models onto autonomous vehicle platforms.
Case Studies and Real-World Applications
- Analyzing incidents in autonomous vehicles and associated safety issues.
- Reviewing successful deployments of AI-driven driving systems.
- Project: Developing a functional lane-following AI model.
Requirements
- Strong proficiency in Python programming.
- Practical experience with machine learning and deep learning frameworks.
- Familiarity with automotive technology and computer vision concepts.
Target Audience
- Data scientists looking to specialize in autonomous driving applications.
- AI experts focused on the development of automotive AI solutions.
- Developers eager to explore deep learning techniques for self-driving vehicles.
21 Hours
