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 levels of autonomous driving and the role of AI integration
- Surveying the AI frameworks and libraries prevalent in autonomous driving
- Reviewing current trends and innovations in AI-driven vehicle autonomy
Core Deep Learning Concepts for Autonomous Driving
- Neural network structures tailored for self-driving cars
- Application of Convolutional Neural Networks (CNNs) in image processing
- Use of Recurrent Neural Networks (RNNs) for handling temporal data
Computer Vision Applications in Autonomous Driving
- Object detection implementation using YOLO and SSD architectures
- Methods for lane detection and road following
- Semantic segmentation for comprehensive environmental perception
Reinforcement Learning for Driving Decisions
- Role of Markov Decision Processes (MDP) in autonomous vehicles
- Training Deep Reinforcement Learning (DRL) models
- Simulation-based approaches for developing driving policies
Sensor Fusion and Perception Systems
- Combining data from LiDAR, RADAR, and cameras
- Techniques involving Kalman filtering and sensor fusion
- Processing multi-sensor data for accurate environment mapping
Deep Learning Models for Driving Prediction
- Creating models for behavioral prediction
- Forecasting trajectories for effective obstacle avoidance
- Recognizing driver state and intent
Model Evaluation and Optimization Strategies
- Key metrics for assessing model accuracy and performance
- Techniques to optimize for real-time execution
- Deploying trained models onto autonomous vehicle platforms
Case Studies and Real-World Implementation
- Analyzing incidents and safety challenges in autonomous vehicles
- Examining successful cases of AI-driven driving systems
- Practical Project: Developing a lane-following AI model
Requirements
- Strong competency in Python programming
- Practical experience with machine learning and deep learning frameworks
- Working knowledge of automotive technology and computer vision
Target Audience
- Data scientists focused on autonomous driving applications
- AI specialists dedicated to automotive AI development
- Developers seeking expertise in deep learning for self-driving vehicles
21 Hours
