自动驾驶AI与深度学习 培训

Introduction to AI in Autonomous Vehicles

• Understanding autonomous driving levels and AI integration
• Overview of AI frameworks and libraries used in autonomous driving
• Trends and innovations in AI-powered vehicle autonomy

Deep Learning Fundamentals for Autonomous Driving

• Neural network architectures for self-driving cars
• Convolutional neural networks (CNNs) for image processing
• Recurrent neural networks (RNNs) for temporal data

Computer Vision for Autonomous Driving

• Object detection using YOLO and SSD
• Lane detection and road following techniques
• Semantic segmentation for environmental perception

Reinforcement Learning for Driving Decisions

• Markov Decision Processes (MDP) in autonomous vehicles
• Training deep reinforcement learning (DRL) models
• Simulation-based learning for driving policies

Sensor Fusion and Perception

• Integrating LiDAR, RADAR, and camera data
• Kalman filtering and sensor fusion techniques
• Multi-sensor data processing for environment mapping

Deep Learning Models for Driving Prediction

• Building behavioral prediction models
• Trajectory forecasting for obstacle avoidance
• Driver state and intent recognition

Model Evaluation and Optimization

• Metrics for model accuracy and performance
• Optimization techniques for real-time execution
• Deploying trained models in autonomous vehicle platforms

Case Studies and Real-World Applications

• Analyzing autonomous vehicle incidents and safety challenges
• Exploring successful implementations of AI-driven driving systems
• Project: Developing a lane-following AI model