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Duration 21 hours
Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring levels of autonomous driving and the role of AI integration
- Review of key AI frameworks and libraries utilized in autonomous driving
- Current trends and innovations driving vehicle autonomy
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures tailored for self-driving cars
- Convolutional Neural Networks (CNNs) for efficient image processing
- Recurrent Neural Networks (RNNs) for handling temporal data
Computer Vision for Autonomous Driving
- Implementing object detection using YOLO and SSD architectures
- Techniques for lane detection and road-following mechanisms
- Applying semantic segmentation for comprehensive environmental perception
Reinforcement Learning for Driving Decisions
- Applying Markov Decision Processes (MDP) to autonomous vehicles
- Training Deep Reinforcement Learning (DRL) models effectively
- Utilizing simulation-based approaches to develop driving policies
Sensor Fusion and Perception
- Combining data from LiDAR, RADAR, and cameras
- Employing Kalman filtering and advanced 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 states and intents
Model Evaluation and Optimization
- Selecting appropriate metrics for assessing model accuracy and performance
- Applying optimization techniques 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
- Examining successful implementations of AI-driven driving systems
- Practical project: Developing and deploying 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 principles
Target Audience
- Data scientists seeking to specialize in autonomous driving applications
- AI professionals dedicated to automotive AI development
- Engineers interested in applying deep learning techniques to self-driving car technology
