Problem

The likelihood of people walking into the street at the same time on both sides of a shopping street is much higher on a weekday at 3:00 pm than on a Sunday. An attentive, considerate driver knows these relationships, estimates what is currently available and then chooses an appropriate driving style. In the first case, the driver would proactively reduce the vehicle speed to prevent the emergence of dangerous situations or an accident. This dynamic risk assessment is an essential function for future autonomous vehicles whose development and safeguards are still lacking the relevant test scenarios for OEMs and suppliers.

To address this challenge, the project team will take the following steps:

• Existing data sets that describe critical driving situations are examined for driving context parameters together with suitable data from the mCLOUD and refined accordingly.
• Through KI-based learning methods, models are developed and trained that generalize these test scenarios. 
• Thus, new variant-rich synthetic test scenarios can be derived and automatically transferred to different test environments. 
• For this purpose, a web portal is developed, which is based on the EDI hive standard platform, and is directly connected to the mCLOUD.