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Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring autonomous driving levels and the role of AI integration.
- Overview of key AI frameworks and libraries utilized in the sector.
- Current trends and innovations driving vehicle autonomy.
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures tailored for self-driving cars.
- Application of Convolutional Neural Networks (CNNs) for image processing.
- Utilizing Recurrent Neural Networks (RNNs) for handling temporal data.
Computer Vision for Autonomous Driving
- Object detection strategies using YOLO and SSD architectures.
- Techniques for lane detection and autonomous road following.
- Semantic segmentation for enhanced environmental perception.
Reinforcement Learning for Driving Decisions
- Application of Markov Decision Processes (MDP) in autonomous contexts.
- Training and refining deep reinforcement learning (DRL) models.
- Simulation-based approaches for developing driving policies.
Sensor Fusion and Perception
- Integration of data from LiDAR, RADAR, and cameras.
- Employing Kalman filtering and advanced sensor fusion techniques.
- Processing multi-sensor data to create accurate environment maps.
Deep Learning Models for Driving Prediction
- Constructing models for behavioral prediction.
- Forecasting trajectories for effective obstacle avoidance.
- Recognizing driver states 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
- Analyzing incidents and safety challenges in autonomous vehicle operations.
- Reviewing successful implementations of AI-driven driving systems.
- Capstone Project: Development of a lane-following AI model.
Requirements
- Strong proficiency in Python programming.
- Practical experience with machine learning and deep learning frameworks.
- Familiarity with automotive technology standards and computer vision concepts.
Target Audience
- Data scientists specializing in autonomous driving applications.
- AI specialists focused on the development of automotive AI solutions.
- Developers seeking to apply deep learning techniques to self-driving car technologies.
21 Hours
