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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
Audience
- Data scientists aiming to work on autonomous driving applications
- AI specialists focusing on automotive AI development
- Developers interested in deep learning techniques for self-driving cars
21 Hours
