Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring the different levels of autonomous driving and how AI integrates into them
- A broad overview of the AI frameworks and libraries commonly used in the field
- Current trends and innovations driving AI-powered vehicle autonomy
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures specifically tailored for self-driving cars
- Applying Convolutional Neural Networks (CNNs) for image processing tasks
- Using Recurrent Neural Networks (RNNs) to handle temporal data streams
Computer Vision for Autonomous Driving
- Implementing object detection with YOLO and SSD architectures
- Techniques for lane detection and reliable road following
- Using semantic segmentation to enhance environmental perception
Reinforcement Learning for Driving Decisions
- Applying Markov Decision Processes (MDP) within autonomous vehicle systems
- Training and refining Deep Reinforcement Learning (DRL) models
- Leveraging simulation-based learning to develop robust driving policies
Sensor Fusion and Perception
- Methods for integrating data from LiDAR, RADAR, and cameras
- Applying Kalman filtering and advanced sensor fusion techniques
- Processing multi-sensor data to create accurate environmental maps
Deep Learning Models for Driving Prediction
- Constructing models that predict vehicle and pedestrian behavior
- Forecasting trajectories to facilitate effective obstacle avoidance
- Recognizing driver state and intent through data analysis
Model Evaluation and Optimization
- Key metrics for assessing model accuracy and overall performance
- Strategies for optimizing models to ensure real-time execution speed
- Deploying trained models onto autonomous vehicle platforms
Case Studies and Real-World Applications
- An in-depth analysis of autonomous vehicle incidents and associated safety challenges
- Reviewing successful implementations of AI-driven driving systems in the wild
- Capstone project: Developing a functional lane-following AI model
Requirements
- Strong command of Python programming
- Hands-on experience with major machine learning and deep learning frameworks
- Knowledge of automotive technology concepts and computer vision fundamentals
Target Audience
- Data scientists seeking to specialize in autonomous driving applications
- AI experts dedicated to advancing automotive AI development
- Developers exploring deep learning techniques for self-driving car technology
21 Hours
