View source for Detection Unit Imperfections and CPM Reliability in Interoperable RSUs

{{StudentProjectTemplate
|Summary=Collaboration with Mittlogik: The project investigates how data and analysis quality affects reliability of collective perception services.
|Keywords=RSU, CPM, reliability analysis, connectivity
|TimeFrame=Spring-Summer 2026
|Prerequisites=Strong skills in C/C++, Python, and data analysis. Knowledge of real-time systems and a basic understanding of V2X (CPM) and sensor fusion concepts is required
|Supervisor=Mittlogik, Elena Haller, Oscar Molina
|Author=Mittlogik
|Level=Master
|Status=Open
}}
<h2>Background</h2>
<p>
Standardization bodies have defined the structure of Vehicle-to-Everything (V2X) messages such as the
<strong>Collective Perception Message (CPM)</strong>. However, the actual safety utility of these messages
depends entirely on the quality of the data provided by the <strong>Detection Unit (DU)</strong>.
In real-world deployments, DUs (e.g., computer vision sensors) suffer from variable latency,
measurement noise, and intermittent detection gaps.
</p>
<p>
This thesis investigates how these <em>sensor imperfections</em> propagate through the
Roadside Unit (RSU) interworking protocol and affect the reliability and timeliness of
safety warnings sent to vehicles.
</p>

<h2>Objectives</h2>
<ol>
  <li>
    <strong>Characterize DU Latency and Noise:</strong>
    Benchmark the latency and spatial accuracy of the selected DU under various conditions,
    such as varying numbers of detected objects and different lighting environments.
  </li>
  <li>
    <strong>Model Detection Imperfections:</strong>
    Develop a fault-injection framework capable of introducing artificial delays, noise,
    and dropped detections into the data stream between the DU and the RSU.
  </li>
  <li>
    <strong>Analyze CPM Reliability:</strong>
    Quantify the impact of detection imperfections on generated CPMs using metrics such as
    Age of Information (AoI), positional error, and message frequency stability.
  </li>
  <li>
    <strong>Evaluate System Robustness:</strong>
    Identify the system break-point, defined as the level of sensor latency or noise at which
    CPMs no longer provide a reliable safety benefit for vulnerable road user (VRU) protection.
  </li>
</ol>

<h2>Methodology</h2>
<ul>
  <li>
    <strong>Benchmarking:</strong>
    Conduct physical experiments to measure the baseline latency of the DU-to-RSU communication link.
  </li>
  <li>
    <strong>Fault Injection Framework:</strong>
    Implement a Python/C++ tool positioned between the DU and the RSU interworking module to simulate
    real-world errors, including noise, jitter, and packet loss.
  </li>
  <li>
    <strong>Stress Testing:</strong>
    Execute safety-critical scenarios, such as a pedestrian emerging from behind a vehicle,
    while injecting controlled levels of sensor imperfection.
  </li>
  <li>
    <strong>Data Analysis:</strong>
    Compare ground-truth pedestrian positions with RSU-reported positions contained in CPMs
    to calculate error margins and safety-critical delays.
  </li>
</ul>

<h2>Expected Deliverables</h2>
<ul>
  <li>
    <strong>Robustness Analysis Report:</strong>
    A comprehensive assessment of how sensor imperfections influence CPM quality and reliability.
  </li>
  <li>
    <strong>DU Performance Profile:</strong>
    A technical benchmark documenting the performance characteristics of the selected detection hardware.
  </li>
  <li>
    <strong>Recommendations for RSU Logic:</strong>
    Proposed software-level mitigation strategies, such as data smoothing or dead reckoning,
    to reduce the impact of sensor limitations.
  </li>
</ul>