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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

  • Competence in Python programming.
  • Prior experience with machine learning and deep learning frameworks.
  • Familiarity with automotive technology and computer vision principles.

Target Audience

  • Data scientists looking to engage in autonomous driving projects.
  • AI specialists dedicated to automotive AI development.
  • Developers interested in applying deep learning techniques to self-driving cars.
 21 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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