ROS 2 Trace Analysis for Semantic Scheduling (Thesis)

This repo provides the data analysis framework used for my thesis
"Semantic Scheduling for Multi-Modal Sensor Data on the Example of ROS 2" (TU Berlin & DLR, 2025).

It is based on TUM-AVS/ros2_latency_analysis (dataflow-analysis branch) by Betz et al.
Please cite their original work when using this code (see below).

Key Features

• Automated extraction of end-to-end and component latencies from ROS 2 traces
• Extended batch processing, including meta-anaylsis
• Scripts and fixes tailored for semantic scheduling experiments

Main Additions & Changes

• New scripts:
  add_csv_header.sh, batch_analysis_analysis.py, csv2table.py, csvfix.sh,
  run_batch_analysis_analysis.sh, run_batch_analyze.sh
• Main modifications:
  batch_analyze.py, trace-analysis.ipynb
• Submodules:
  Includes ros2_tracing and tracetools_analysis

All changes are documented in the git history.

Setup

• Requires Python 3.10+, ROS 2, JupyterLab, and ros2_tracing
• Install dependencies:
  pip3.10 install -r requirements.txt
• Run interactive analysis in Jupyter (trace-analysis.ipynb) or batch via provided scripts.

See the upstream README for more details on prerequisites and methodology.

Usage

First, make sure all your traces are in sub-directories conforming to the common experiment type, e.g.:

traces/
  ros_multi/
    ros_multi_1
    ros_multi_2
    ...
    ros_multi_n
  ros_single/
    ros_single_1
    ros_single_2
    ...
    ros_single_m

Then use run_batch_analyze.sh to run the analysis (trace-analysis.ipynb) for the traces you gathered. This will create a summarizing results.csv in the respecitve directory (here thus traces/ros_multi/ and 'traces/ros_multi/').
The csv will not have headers, to add them, use add_csv_header.sh.
Now you can run run_batch_analysis_analysis.sh to generate the end-to-end analysis box-plots from that results.csv (will also generate a results_summary.csv). The latter is ideal to be turned into LaTeX tables, using csv2table.py.

Output

• Latency CSVs (E2E and per-chain), plots (PDF/PNG), and LaTeX-ready tables

Citation

If you use this code, please cite:

Betz, T. et al., “Latency Measurement for Autonomous Driving Software Using Data Flow Extraction,”
IEEE Intelligent Vehicles Symposium (IV), 2023.
DOI: 10.1109/iv55152.2023.10186686

@inproceedings{Betz2023,
  doi = {10.1109/iv55152.2023.10186686},
  url = {https://doi.org/10.1109/iv55152.2023.10186686},
  year = {2023},
  author = {Tobias Betz and Maximilian Schmeller and Andreas Korb and Johannes Betz},
  title = {Latency Measurement for Autonomous Driving Software Using Data Flow Extraction},
  booktitle = {2023 {IEEE} Intelligent Vehicles Symposium ({IV})}
}

License

See upstream for original license; thesis-specific additions are for academic/non-commercial use.