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Course Outline
Introduction to AI in Autonomous Vehicles
- Examining levels of autonomous driving and AI integration
- Survey of AI frameworks and libraries prevalent in autonomous driving
- Emerging trends and innovations in AI-driven vehicle autonomy
Deep Learning Foundations for Autonomous Driving
- Neural network architectures suited for self-driving cars
- Convolutional neural networks (CNNs) for image processing
- Recurrent neural networks (RNNs) for handling temporal data
Computer Vision for Autonomous Driving
- Object detection using YOLO and SSD
- Lane detection and road-following methodologies
- Semantic segmentation for environmental awareness
Reinforcement Learning for Driving Decisions
- Markov Decision Processes (MDP) in autonomous vehicle contexts
- Training deep reinforcement learning (DRL) models
- Simulation-based approaches for developing driving policies
Sensor Fusion and Perception
- Combining LiDAR, RADAR, and camera data
- Kalman filtering and advanced sensor fusion techniques
- Multi-sensor data processing for environmental mapping
Deep Learning Models for Driving Prediction
- Creating behavioural prediction models
- Trajectory forecasting for obstacle avoidance
- Recognising driver state and intent
Model Evaluation and Optimisation
- Key metrics for assessing model accuracy and performance
- Optimisation techniques for real-time execution
- Deploying trained models onto autonomous vehicle platforms
Case Studies and Practical Applications
- Reviewing autonomous vehicle incidents and safety considerations
- Investigating successful deployments 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
- Working knowledge of automotive technology and computer vision
Target Audience
- Data scientists seeking to specialise in autonomous driving applications
- AI experts focused on the development of automotive AI
- Developers exploring deep learning techniques for self-driving vehicles
21 Hours
