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Course Outline
Introduction to AI in Autonomous Vehicles
- Understanding autonomous driving levels and AI integration.
- Overview of AI frameworks and libraries used in autonomous driving.
- Trends and innovations in AI-powered vehicle autonomy.
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures for self-driving cars.
- Convolutional neural networks (CNNs) for image processing.
- Recurrent neural networks (RNNs) for temporal data.
Computer Vision for Autonomous Driving
- Object detection using YOLO and SSD.
- Lane detection and road following techniques.
- Semantic segmentation for environmental perception.
Reinforcement Learning for Driving Decisions
- Markov Decision Processes (MDP) in autonomous vehicles.
- Training deep reinforcement learning (DRL) models.
- Simulation-based learning for driving policies.
Sensor Fusion and Perception
- Integrating LiDAR, RADAR, and camera data.
- Kalman filtering and sensor fusion techniques.
- Multi-sensor data processing for environment mapping.
Deep Learning Models for Driving Prediction
- Building behavioral prediction models.
- Trajectory forecasting for obstacle avoidance.
- Driver state and intent recognition.
Model Evaluation and Optimization
- Metrics for model accuracy and performance.
- Optimization techniques for real-time execution.
- Deploying trained models in autonomous vehicle platforms.
Case Studies and Real-World Applications
- Analyzing autonomous vehicle incidents and safety challenges.
- Exploring successful implementations of AI-driven driving systems.
- Project: Developing a lane-following AI model.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Experience with machine learning and deep learning frameworks.
- Familiarity with automotive technology and computer vision.
Target Audience
- Data scientists aiming to contribute to autonomous driving applications.
- AI specialists dedicated to automotive AI development.
- Developers interested in applying deep learning techniques to self-driving cars.
21 Hours
