From 1st December 2020 we will be located in the industrial park Gilching.
MESSRING Playing Child Target - for easy performance of AEB VRU pedestrian back-over tests
This year MESSRING was again exhibiting in its well-known manner at SafetyWeek in Würzburg. On a total of three days the latest product developments were showcased to all participants
The weather has the greatest impact on the detection of the environment - and thus on the safety of autonomous vehicles and driver assistance systems - via camera, radar and lidar. For the further development of sensors and the active safety of vehicles, MESSRING now offers an effective test tool for all suppliers and developers of sensors and driver assistance systems...
In the first session, our lighting experts have already explained how lighting systems at MESSRING are planned for crash or sled test facility operation and the particular advantages of applying M=LIGHT LED lamps. We have asked Max, Stefan, Benjamin and Justinas again for an interview. This time we wanted to find out why these high amounts of light are necessary at all and what exactly the point of "flashing" and synchronizing flash and camera is.
The new Euro NCAP test protocol came into force at the turn of the year. More than before, the protection of crash partners is taken into account when evaluating the results.
As hybrid and fully-electric powertrains continue to gain market share in both the passenger car and bus segments, the operators of crash-test facilities are facing specific new challenges...
A group of 21 pupils used their free day at school to inform themselves about regional training opportunities
Our HR-team with support from the sales and construction departments participated in this year's university contact fair HOKO.
The right lighting technology for crash test trials means: controllable, reproducible and extremely bright! A recurring topic for traffic experts...
The practical suitability of semi- and fully autonomous vehicles is often determined by many test-drive kilometers on public roads. While this method very effectively allows the vehicle to “learn” all real-world situations, the replication of desired situations is not guaranteed and can therefore result in hours of driving with very little new or useful data being gained.