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Course Outline

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

Summary and Next Steps

Requirements

  • Strong proficiency in Python programming.
  • Hands-on experience with machine learning and deep learning frameworks.
  • Familiarity with automotive technology and computer vision concepts.

Target Audience

  • Data scientists interested in developing autonomous driving applications.
  • AI professionals dedicated to automotive artificial intelligence development.
  • Developers keen on exploring deep learning techniques for self-driving cars.
 21 Hours

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