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Duration 21 hours
Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring the levels of autonomous driving and AI integration.
- Surveying the AI frameworks and libraries commonly used in the industry.
- Reviewing current trends and innovations in AI-driven vehicle autonomy.
Deep Learning Foundations for Autonomous Driving
- Designing neural network architectures suitable for self-driving cars.
- Applying Convolutional Neural Networks (CNNs) for image processing tasks.
- Utilizing Recurrent Neural Networks (RNNs) for handling temporal data.
Computer Vision in Autonomous Driving
- Implementing object detection using YOLO and SSD architectures.
- Developing lane detection and road-following algorithms.
- Using semantic segmentation for comprehensive environmental perception.
Reinforcement Learning for Driving Decisions
- Understanding Markov Decision Processes (MDP) in the context of autonomous vehicles.
- Training Deep Reinforcement Learning (DRL) models effectively.
- Employing simulation-based learning to refine driving policies.
Sensor Fusion and Perception
- Combining data from LiDAR, RADAR, and cameras.
- Applying Kalman filtering and other sensor fusion techniques.
- Processing multi-sensor data for accurate environment mapping.
Deep Learning Models for Driving Prediction
- Constructing models for behavioral prediction.
- Forecasting trajectories to facilitate obstacle avoidance.
- Recognizing driver state and intent.
Model Evaluation and Optimization
- Defining metrics for assessing model accuracy and performance.
- Applying optimization techniques to ensure real-time execution.
- Deploying trained models onto autonomous vehicle platforms.
Case Studies and Real-World Applications
- Analyzing incidents in autonomous vehicles and associated safety challenges.
- Examining successful implementations of AI-driven driving systems.
- Project work: Developing an AI model for lane following.
Requirements
- Strong proficiency in Python programming.
- Practical experience with machine learning and deep learning frameworks.
- A solid understanding of automotive technology and computer vision principles.
Target Audience
- Data scientists seeking to specialize in autonomous driving applications.
- AI specialists focused on the development of automotive AI solutions.
- Developers eager to apply deep learning techniques to self-driving car technologies.
