B.Sc. Thesis – Philipp Zeni – Precision in Motion: ML-Enhanced Race Course Identification for Formula Student Racing


Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 30th October 2023


Theoretical difficulty: mid
Practical difficulty: High


This thesis explores machine learning techniques for analysing onboard recordings from the TU Graz Racing Team, a prominent Formula Student team. The main goal is to design and train an end-to-end machine learning model to autonomously discern race courses based on sensor observations.

Further, this thesis seeks to address the following research questions:

  • Can track markers (cones) be reliably detected and segmented from onboard recordings?
  • Does the delineated racing track provide an adequate level of accuracy to support autonomous driving, minimizing the risk of accidents?
  • How well does a neural network trained on simulated data adapt to real-world situations?
  • Can the neural network ensure real-time processing in high-speed scenarios surpassing 100 km/h?

Tentative Work Plan

To achieve the objectives, the following concrete tasks will be focused on:

  • Thesis initialisation and literature review:
    • Define the scope and boundaries of your work.
    • Study the existing project in [1]  and [2] to identify gaps and methodologies.
  • Setup and familiarize with the simulation environment
    • Build the car model (URDF) for the simulation (optional if you wish to use the existing one)
    • Setup the ROS framework for the simulation (Gazebo, Rviz)
    • Recommended programming tools: C++, Python, Matlab
  • Data acquisition and preprocessing (3D Lidar and RGB-D data)
    • Collect onboard recordings and sensor data from the TU Graz Racing track.
    • Augment the data with additional simulated recordings using ROS, if necessary.
    • Preprocess and label the data for machine learning (ML). This includes segmenting tracks, markers, and other relevant features.
  • Intermediate presentation:
    • Present the results of the literature study or what has been done so far
    • Detailed planning of the next steps
  • ML Model Development:
    •  Design the initial neural network architecture.
    • Train the model using the preprocessed data.
    • Evaluate model performance using metrics like accuracy, precision, recall, etc.
    • Iteratively refine the model based on the evaluation results.
  • Real-world Testing (If Possible):
    • Implement the trained model on a real vehicle’s onboard computer.
    • Test the vehicle in a controlled environment, ensuring safety measures are in place.
    • Analyze and compare the model’s performance in real-world scenarios versus simulations.
  • Optimization for Speed and Efficiency (Optional):
    • Validate the model to identify bottlenecks.
    • Optimize the neural network for real-time performance, especially for high-speed scenarios
  • Documentation and B.Sc. thesis writing:
    • Document the entire process, methodologies, and tools used.
    • Analyze and interpret the results.
    • Draft the thesis, ensuring that at least two of the research questions are addressed.
  • Research paper writing (optional)

Related Work

[1]   Autonomous Racing Graz, “Enhanced localisation for autonomous racing with high-resolution lidar“, Article by Tom Grey, Visited 30.10.2023.

[2]   Autonomous RC car racing ETH Zürich, “The ORCA (Optimal RC Racing) Project“, Article by Alex Liniger, Visited 30.10.2023.

[3]   P. Cai, H. Wang, H. Huang, Y. Liu and M. Liu, “Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning,” in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7262-7269, Oct. 2021, doi: 10.1109/LRA.2021.3097345.

[4]   Z. Lu, C. Zhang, H. Zhang, Z. Wang, C. Huang and Y. Ji, “Deep Reinforcement Learning Based Autonomous Racing Car Control With Priori Knowledge,” 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 2241-2246, doi: 10.1109/CAC53003.2021.9728289.

[5]   J. Kabzan, L. Hewing, A. Liniger and M. N. Zeilinger, “Learning-Based Model Predictive Control for Autonomous Racing,” in IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3363-3370, Oct. 2019, doi: 10.1109/LRA.2019.2926677.