Research Project

Autonomous emergency maneuvering and movement monitoring for road transport security

Terror attacks in Berlin and Nice have shown drastically which devastating damages can be caused by trucks, when they are abused by terrorist and driven into crowded areas.
The goal of the Research Project TransSec is to develop a system, which is capable to prevent such misuses of trucks. Present security systems in the vehicles can be deactivated by the driver, which allows terrorist to use them as a weapon against people. To prevent this in future TransSec aims to create a system which will permanently monitor the vehicle and, in case of critical driving maneuvers, automatically start emergency routines to reduce the arising danger.

These emergency routines cannot be disabled by the driver, to prevent the misuse of the truck. The following technology is combined within the project:

  • Multi-sensor positioning system
  • European satellite navigation technology
  • precise, digital street maps
  • environment recognition
  • scene understanding and risk analysis
  • autonomous driving maneuvering
  • communication of the truck with its environment
Transsec logo
Transsec logo

Robust and reliable positioning system

One core feature of this new security system will be a robust and reliable positioning system. Within the positioning system the GNSS module will be improved by the newest developments of the Galileo navigation satellite system like signal authentication to detect spoofing, jamming and other manipulations. Additionally the positioning system will be complemented by inertial sensors and odometer data to ensure a reliable position even in challenging urban environments like city canyons.

To detect forbidden driving maneuvers, like driving in pedestrian zones or making prohibited turns, information of the digital street map will be used. To do so the coordinate from the positioning system will be used to locate the truck in the map. With use of the sensors for the environment recognition, like cameras and Lidar sensors, the static features of the map will be extended to a so-called local dynamic map with the current dynamic objects around the truck, like pedestrians or other cars. Based on this enhanced map data and the motion tracking of the surrounding objects and persons a risk analysis be carried out. If critical driving maneuvers gets detected, the security system will intervene to reduce the danger. Additionally the surrounding is going to be warned through a vehicle to everything communication (V2X-technology) about the arising danger.

Poster Project overview

Project partners

The Institute of Engineering Geodesy is mainly responsible for the positioning system and also the digital street map workpackages. For the latter we are the workpackage leader.

  • Daimler AG (Coordinator), DE
  • TeleConsult Austria GmbH, AT
  • Vicomtech, ES
  • Waterford Institute of Technology, IR
  • University of Stuttgart, Institute of Engineering Geodesy, DE


European GNSS Agency

H2020-GALILEO-GSA-2017 Innovation Action
Grant Agreement Nr.:776355

February 2018 to February 2021

Open Research Data for digital road map evaluation


Reference trajectories for digital road map evaluation

Related Publication:

Zhang, L.; Wang, J.; Wachsmuth, M; Gasparac, M.; Trauter, R.; Schwieger, V.: Role of Digital Maps in Road Transport Security. FIG Working Week 2019, Hanoi, Vietnam.

Description of open access research data:

147 km of precise GNSS trajectories are available. They have a sampling rate of 1 Hz and were generated with a Leica Viva GS15 receiver (processing of phase data; accuracy better than 10 cm). The positions and their standard deviations are provided in an ASCII Format. The trajectories are organized as follows: 17.3 km German Motorway, 50.2 km motorway entrance and exit ramps, 79.5 km urban areas. The data include a time stamp in GPS-time, the 2-dimensional positions in North, East (UTM) and additionally the ellipsoidal height as well as the respective standard deviations. Additionally the information on which lane the reference was generated is given. The lane changing and where GPS outliers occurred are marked too.

The amount of data is around 2.7 MB.

If you are interested in this data, please contact


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