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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring autonomous driving levels and AI integration strategies
- Overview of AI frameworks and libraries prevalent in autonomous driving
- Emerging trends and innovations in AI-driven vehicle autonomy
Deep Learning Essentials for Autonomous Driving
- Neural network architectures tailored for self-driving cars
- Convolutional Neural Networks (CNNs) for advanced image processing
- Recurrent Neural Networks (RNNs) for handling temporal data
Computer Vision in Autonomous Driving
- Object detection techniques using YOLO and SSD
- Methods for lane detection and road tracking
- Semantic segmentation for comprehensive environmental perception
Reinforcement Learning for Driving Decisions
- Applying Markov Decision Processes (MDP) in 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 streams
- Techniques for Kalman filtering and sensor fusion
- Processing multi-sensor data for accurate environment mapping
Deep Learning Models for Driving Prediction
- Developing models for behavioral prediction
- Trajectory forecasting to ensure obstacle avoidance
- Recognizing driver state and intent
Model Evaluation and Optimization
- Assessing model accuracy and performance metrics
- Optimizing models for real-time execution efficiency
- Deploying trained models onto autonomous vehicle platforms
Case Studies and Real-World Applications
- Analyzing autonomous vehicle incidents and associated safety challenges
- Reviewing successful implementations of AI-driven driving systems
- Practical Project: Developing a lane-following AI model
Requirements
- Proficiency in Python programming
- Hands-on experience with machine learning and deep learning frameworks
- Knowledge of automotive technology and computer vision principles
Target Audience
- Data scientists looking to specialize in autonomous driving applications
- AI specialists focused on developing automotive AI systems
- Developers seeking to apply deep learning techniques to self-driving vehicles
21 Hours
