Identification and Visualization of Accident Hotspots on the BeIntelli Test Track and Recommendation for Interventions with Infrastructure Sensors

The BeIntelli project led by the DAI-Labor is a showcase project for the autonomous mobility of the future. Based on real-life scenarios, future mobility solutions are tested and evaluated in the BeIntelli real lab in the heart of Berlin. A particular focus is on the representation of networked, cooperative automated mobility (CCAM). For this, we consider the entire infrastructure by digitizing vehicles and the infrastructure (edge) and establishing communication between vehicles, infrastructure, and cloud, for example, to enable perception over the horizon. For this purpose, we are digitizing the infrastructure in the BeIntelli test track in the heart of Berlin, which extends from the Brandenburg Gate to Großer Stern, Ernst-Reuter-Platz, Hardenbergstr., Kurfürstendamm and Adenauerplatz, among other places.

In order to make mobility safer in the future and at the same time enable automated driving for all parties involved, the targeted support of vehicles by the infrastructure, e.g., in the perception of pedestrians, cyclists, etc., is one way to increase safety. This is especially reasonable at accident hotspots. This thesis shall based on the data provided by the Unfallatlas Deutschland focus on analyzing the BeIntelli test track with respect to accident hotspots, put it in relation to a suitable comparison track in Berlin, and discuss possible locations and types of infrastructure sensors for supporting automated driving vehicles.

Research focus and tasks

  • Search for further data sources for the evaluation of accidents
  • Analysis and presentation of data from the accident atlas (and other sources) for the BeIntelli test track and suitable reference tracks
  • Identification of accident black spots and types on the route based on historical data
  • Discussion on the targeted incorporation of infrastructure sensor technology for the prevention of accidents


  • Experience in data analysis is necessary
  • Experience with map-based data analysis is beneficial
  • Programming experience in Python is necessary
  • Knowledge of transportation, topologies, and traffic systems is helpful
  • High level of motivation to work independently and in a solution-oriented manner

Interested? Get in touch.

We are looking forward to your application. Please send us a detailed application via e-mail, including your current transcript of records, and curriculum vitae in tabular form, and go into the requirements; please send all attached documents as PDF files.