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
- Examining autonomous driving levels and the integration of AI
- An overview of AI frameworks and libraries utilized in autonomous driving
- Current trends and innovations in AI-driven vehicle autonomy
Deep Learning Foundations for Autonomous Driving
- Neural network architectures designed for self-driving cars
- Application of Convolutional Neural Networks (CNNs) for image processing
- Use of Recurrent Neural Networks (RNNs) for handling temporal data
Computer Vision for Autonomous Driving
- Object detection utilizing YOLO and SSD models
- Techniques for lane detection and road following
- Semantic segmentation for improved environmental perception
Reinforcement Learning for Driving Decisions
- Markov Decision Processes (MDP) in the context of autonomous vehicles
- Training Deep Reinforcement Learning (DRL) models
- Learning driving policies through simulation-based approaches
Sensor Fusion and Perception
- Integration of LiDAR, RADAR, and camera data
- Kalman filtering and advanced sensor fusion techniques
- Processing multi-sensor data for comprehensive environment mapping
Deep Learning Models for Driving Prediction
- Developing behavioral prediction models
- Forecasting trajectories to facilitate obstacle avoidance
- Recognizing driver state and intent
Model Evaluation and Optimization
- Metrics for assessing model accuracy and performance
- Optimization strategies for real-time execution
- Deployment of trained models onto autonomous vehicle platforms
Case Studies and Real-World Applications
- Analyzing incidents and safety challenges in autonomous vehicles
- Exploring successful case studies of AI-driven driving systems
- Practical project: Developing a lane-following AI model
Requirements
- Strong proficiency in Python programming
- Practical experience with machine learning and deep learning frameworks
- A solid understanding of automotive technology and computer vision
Target Audience
- Data scientists seeking to specialize in autonomous driving applications
- AI experts concentrated on developing automotive AI solutions
- Developers keen on applying deep learning techniques to self-driving cars
21 Hours
