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Course Outline
Introduction to AI in Autonomous Vehicles
- Examining the levels of autonomous driving and the integration of AI
- Overview of key AI frameworks and libraries utilized in autonomous driving
- Current trends and innovations in AI-driven vehicle autonomy
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures suitable for self-driving cars
- Convolutional neural networks (CNNs) for advanced image processing
- Recurrent neural networks (RNNs) for handling temporal data
Computer Vision for Autonomous Driving
- Object detection utilizing YOLO and SSD architectures
- Techniques for lane detection and road following
- Semantic segmentation for comprehensive environmental perception
Reinforcement Learning for Driving Decisions
- Application of Markov Decision Processes (MDP) in autonomous vehicles
- Training deep reinforcement learning (DRL) models
- Simulation-based approaches for developing driving policies
Sensor Fusion and Perception
- Integrating data from LiDAR, RADAR, and cameras
- Implementing Kalman filtering and sensor fusion techniques
- Multi-sensor data processing for accurate environment mapping
Deep Learning Models for Driving Prediction
- Creating models for behavioral prediction
- Trajectory forecasting aimed at obstacle avoidance
- Recognizing driver state and intent
Model Evaluation and Optimization
- Key metrics for assessing model accuracy and performance
- Optimization strategies 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
- Exploring successful real-world implementations of AI-driven driving systems
- Project: Development of 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 concepts
Target Audience
- Data scientists seeking to specialize in autonomous driving applications
- AI specialists dedicated to the development of automotive AI systems
- Developers interested in applying deep learning techniques to self-driving vehicles
21 Hours
