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Course Outline
Introduction to AI in Autonomous Vehicles
- Understanding autonomy levels and the role of AI integration
- Overview of key AI frameworks and libraries in autonomous driving
- Current trends and innovations in AI-driven vehicle autonomy
Foundations of Deep Learning 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 Applications in Autonomous Driving
- Object detection using YOLO and SSD architectures
- Techniques for lane detection and road following
- Semantic segmentation for environmental awareness
Reinforcement Learning for Driving Decisions
- Markov Decision Processes (MDP) in the context of autonomous vehicles
- Training Deep Reinforcement Learning (DRL) models
- Simulation-based approaches for learning driving policies
Sensor Fusion and Perception
- Combining data from LiDAR, RADAR, and cameras
- Application of Kalman filtering and sensor fusion methods
- Processing multi-sensor data for environmental mapping
Deep Learning Models for Driving Prediction
- Creating models for behavioural prediction
- Forecasting trajectories to aid obstacle avoidance
- Recognising driver state and intent
Model Evaluation and Optimization
- Key metrics for assessing model accuracy and performance
- Optimisation techniques for real-time execution
- Deployment of trained models onto autonomous vehicle platforms
Case Studies and Real-World Applications
- Analysis of autonomous vehicle incidents and safety considerations
- Review of successful AI-driven driving system implementations
- Capstone project: Developing an AI model for lane following
Requirements
- Strong command of Python programming
- Practical experience with machine learning and deep learning frameworks
- Awareness of automotive technology and computer vision concepts
Target Audience
- Data scientists aspiring to specialise in autonomous driving applications
- AI specialists concentrating on the development of automotive AI
- Developers keen on applying deep learning techniques to self-driving vehicles
21 Hours
