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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring the levels of autonomous driving and the role of AI integration
- Overview of key AI frameworks and libraries used in the autonomous driving sector
- Current trends and innovations driving AI-powered vehicle autonomy
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures tailored for self-driving cars
- Using Convolutional Neural Networks (CNNs) for image processing
- Applying Recurrent Neural Networks (RNNs) for handling temporal data
Computer Vision for Autonomous Driving
- Detecting objects using YOLO and SSD algorithms
- Techniques for lane detection and road following
- Utilizing semantic segmentation for enhanced environmental perception
Reinforcement Learning for Driving Decisions
- Application of Markov Decision Processes (MDP) in autonomous vehicles
- Training Deep Reinforcement Learning (DRL) models
- Simulation-based learning for developing effective driving policies
Sensor Fusion and Perception
- Integrating data from LiDAR, RADAR, and cameras
- Employing Kalman filtering and sensor fusion techniques
- Processing multi-sensor data for accurate environment mapping
Deep Learning Models for Driving Prediction
- Developing behavioral prediction models
- Forecasting trajectories for effective 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
- Analysis of autonomous vehicle incidents and associated safety challenges
- Review of successful implementations of AI-driven driving systems
- Project: Developing a functional 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 looking to work on autonomous driving applications
- AI specialists concentrating on automotive AI development
- Developers interested in applying deep learning techniques to self-driving vehicles
21 Hours
