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Course Outline
Introduction to AI in Autonomous Vehicles for Government
- Understanding the levels of autonomous driving and the integration of artificial intelligence (AI)
- Overview of AI frameworks and libraries utilized in autonomous driving systems
- Current trends and innovations in AI-powered vehicle autonomy for government applications
Deep Learning Fundamentals for Autonomous Driving for Government
- Architectures of neural networks designed for self-driving cars
- Convolutional neural networks (CNNs) for image processing in autonomous vehicles
- Recurrent neural networks (RNNs) for handling temporal data in driving scenarios
Computer Vision for Autonomous Driving for Government
- Object detection techniques using YOLO and SSD algorithms
- Lane detection and road following methodologies
- Semantic segmentation for enhanced environmental perception in government vehicles
Reinforcement Learning for Driving Decisions for Government
- Application of Markov Decision Processes (MDP) in autonomous vehicle decision-making
- Training deep reinforcement learning (DRL) models for driving policies
- Simulation-based training methods for improving driving decisions
Sensor Fusion and Perception for Government
- Integration of LiDAR, RADAR, and camera data in government vehicles
- Kalman filtering techniques for sensor fusion
- Multi-sensor data processing for environment mapping in autonomous government vehicles
Deep Learning Models for Driving Prediction for Government
- Development of behavioral prediction models for government applications
- Trajectory forecasting to enhance obstacle avoidance in government vehicles
- Recognition of driver state and intent for improved safety in government fleets
Model Evaluation and Optimization for Government
- Metrics for assessing model accuracy and performance in government settings
- Optimization techniques to ensure real-time execution of models in government vehicles
- Deployment strategies for integrating trained models into autonomous vehicle platforms used by the government
Case Studies and Real-World Applications for Government
- Analysis of incidents involving autonomous vehicles and associated safety challenges in government operations
- Examination of successful implementations of AI-driven driving systems in government fleets
- Project: Development of a lane-following AI model for government use
Summary and Next Steps for Government
Requirements
- Proficiency in Python programming
- Experience with machine learning and deep learning frameworks
- Familiarity with automotive technology and computer vision
Audience
- Data scientists seeking to work on autonomous driving applications for government
- AI specialists concentrating on the development of automotive AI for government use
- Developers interested in applying deep learning techniques to self-driving car technologies for government projects
21 Hours
