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Course Outline
Introduction to AI in Autonomous Vehicles
- Understanding the levels of autonomous driving and the role of AI integration.
- An overview of the AI frameworks and libraries commonly utilized in autonomous driving.
- Current trends and innovations driving AI-powered vehicle autonomy.
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures specifically designed for self-driving cars.
- Application of Convolutional Neural Networks (CNNs) for image processing.
- Use of Recurrent Neural Networks (RNNs) for handling temporal data.
Computer Vision for Autonomous Driving
- Object detection using YOLO and SSD frameworks.
- Techniques for lane detection and road following.
- Applying semantic segmentation for environmental perception.
Reinforcement Learning for Driving Decisions
- The role of Markov Decision Processes (MDP) in autonomous vehicles.
- Training Deep Reinforcement Learning (DRL) models.
- Implementing simulation-based learning for driving policies.
Sensor Fusion and Perception
- Integrating data from LiDAR, RADAR, and cameras.
- Applying Kalman filtering and sensor fusion techniques.
- Processing multi-sensor data for accurate environment mapping.
Deep Learning Models for Driving Prediction
- Constructing behavioral prediction models.
- Forecasting trajectories for obstacle avoidance.
- Recognizing driver state and intent.
Model Evaluation and Optimization
- Key metrics for assessing model accuracy and performance.
- Techniques for optimizing models for real-time execution.
- Deploying trained models onto autonomous vehicle platforms.
Case Studies and Real-World Applications
- Analyzing incidents in autonomous vehicles and associated safety challenges.
- Examining successful implementations of AI-driven driving systems.
- Project: Developing a lane-following AI model.
Requirements
- Proficiency in Python programming.
- Experience with machine learning and deep learning frameworks.
- Familiarity with automotive technology and computer vision concepts.
Audience
- Data scientists looking to specialize in autonomous driving applications.
- AI specialists focused on developing automotive AI solutions.
- Developers interested in applying deep learning techniques to self-driving cars.
21 Hours
