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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring the levels of autonomous driving and the role of AI integration.
- An overview of the AI frameworks and libraries prevalent in the autonomous driving sector.
- Examining current trends and innovations in AI-powered vehicle autonomy.
Deep Learning Fundamentals for Autonomous Driving
- Examining neural network architectures tailored for self-driving cars.
- Utilizing Convolutional Neural Networks (CNNs) for advanced image processing.
- Applying Recurrent Neural Networks (RNNs) to handle temporal data streams.
Computer Vision for Autonomous Driving
- Implementing object detection using YOLO and SSD architectures.
- Techniques for lane detection and robust road following.
- Employing semantic segmentation for comprehensive environmental perception.
Reinforcement Learning for Driving Decisions
- Understanding Markov Decision Processes (MDP) within autonomous vehicle systems.
- Training Deep Reinforcement Learning (DRL) models for complex tasks.
- Using simulation-based learning to develop effective 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 to create accurate environment maps.
Deep Learning Models for Driving Prediction
- Developing models for behavioral prediction in driving scenarios.
- Forecasting trajectories to facilitate effective obstacle avoidance.
- Recognizing driver state and intent for improved system response.
Model Evaluation and Optimization
- Assessing model accuracy and performance using key metrics.
- Applying optimization techniques to ensure real-time execution efficiency.
- Deploying trained models onto autonomous vehicle platforms.
Case Studies and Real-World Applications
- Analyzing autonomous vehicle incidents to identify safety challenges.
- Reviewing successful implementations 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 professionals focused on developing automotive AI solutions.
- Developers eager to apply deep learning techniques to self-driving vehicles.
21 Hours
