Semantic Monitoring and Copilot for Test drives

In this project we dive into the automotive industry by developing a platform that monitors test drives. The actual task is to develop applications that take e.g., the video-feed from a camera installed in a car and interpreting the data using traditional algorithms or machine learning. Example apps could be: weather detection, pedestrian detection, digit detection on the dashboard of the car, traffic signs detection and more. Also different multimodal sensoric inputs can be used e.g., a accelerometer, gyrometer, light sensors or microphones. This project is a collaboration of Pro2Future, TU Graz and AVL, hence could lead to possible further job opportunities.

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Research area:

  • Cognitive Products

Goal and Tasks:

  • Implement a specific application in the ROS2 framework: Pedestrian detection, maneuver detection, anomaly detection, etc.
  • Research on state-of-the-art techniques for this specific problem, and compare and evaluate them.

Recommended Prior Knowledge:

  • Basic programming skills, such as Python, C, or C++.
  • Basic skill with AI/ML models and frameworks, e.g., Keras, PyTorch, TensorFlow, Scitkit-learn, XGBoost.
  • Optional: Experience with ROS2


  • a.s.a.p.

Duration in months:

  • 6-12 months