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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring autonomous driving levels and AI integration
- Reviewing AI frameworks and libraries employed in autonomous driving
- Examining trends and innovations in AI-driven vehicle autonomy
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures tailored for self-driving cars
- Convolutional neural networks (CNNs) for image processing
- Recurrent neural networks (RNNs) for handling 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 perception
Reinforcement Learning for Driving Decisions
- Markov Decision Processes (MDP) applied to autonomous vehicles
- Training deep reinforcement learning (DRL) models
- Simulation-based learning for developing driving policies
Sensor Fusion and Perception
- Combining LiDAR, RADAR, and camera data
- Kalman filtering and sensor fusion methodologies
- Multi-sensor data processing for environment mapping
Deep Learning Models for Driving Prediction
- Creating behavioral prediction models
- Trajectory forecasting for obstacle avoidance
- Recognizing driver state and intent
Model Evaluation and Optimization
- Metrics for assessing model accuracy and performance
- Optimization strategies for real-time execution
- Deployment of trained models on autonomous vehicle platforms
Case Studies and Real-World Applications
- Analyzing incidents and safety challenges in autonomous vehicles
- Reviewing successful implementations of AI-driven driving systems
- Project: Developing a lane-following AI model
Requirements
- Strong proficiency in Python programming
- Practical experience with machine learning and deep learning frameworks
- Knowledge of automotive technology and computer vision
Target Audience
- Data scientists seeking to specialize in autonomous driving applications
- AI experts concentrated on automotive AI development
- Developers eager to apply deep learning techniques to self-driving vehicles
21 Hours
