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Duration 21 hours
Course Outline
Introduction to AI in Autonomous Vehicles
- Exploring autonomous driving levels and the integration of AI
- Survey of AI frameworks and libraries prevalent in autonomous driving
- Current trends and innovations driving vehicle autonomy through AI
Deep Learning Fundamentals for Autonomous Driving
- Neural network architectures 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 for Autonomous Driving
- Object detection utilizing YOLO and SSD architectures
- Techniques for lane detection and road following
- Semantic segmentation for enhanced environmental perception
Reinforcement Learning for Driving Decisions
- Implementation of Markov Decision Processes (MDP) in autonomous vehicles
- Training strategies for deep reinforcement learning (DRL) models
- Simulation-based approaches for developing driving policies
Sensor Fusion and Perception
- Synthesis of LiDAR, RADAR, and camera data
- Application of Kalman filtering and sensor fusion methodologies
- Processing multi-sensor data for accurate environment mapping
Deep Learning Models for Driving Prediction
- Construction of behavioral prediction models
- Trajectory forecasting strategies for obstacle avoidance
- Recognition of driver state and intent
Model Evaluation and Optimization
- Key metrics for assessing model accuracy and performance
- Optimization strategies for real-time execution efficiency
- Deployment of trained models onto autonomous vehicle platforms
Case Studies and Real-World Applications
- Analysis of autonomous vehicle incidents and associated safety challenges
- Review of successful AI-driven driving system implementations
- Capstone Project: Development of a lane-following AI model
Requirements
- Strong proficiency in Python programming
- Practical experience with machine learning and deep learning frameworks
- Knowledge of automotive technology and computer vision principles
Target Audience
- Data scientists aspiring to specialize in autonomous driving solutions
- AI experts dedicated to advancing automotive AI development
- Developers exploring deep learning applications for self-driving vehicles
