Friday, March 10, 2023
Artificial generation of complex driving scenarios for safety assurance of automated vehicle
Topic and Goal of the Thesis
Vehicles drive increasingly automated and are therefore tested within simulations. Since real input data for tests is expensive and limited available, driving scenarios are increasingly generated synthetically.
However, the scenarios generated are mostly simplistic and do not adequately reflect the complexity in urban space.
In this thesis, a methodology for the realistic generation of complex driving scenarios shall be developed. For this purpose, different road users are to be modeled in order to be able to test an automated driving function in realistic scenarios.
- Literature research on generation of driving scenarios
- Development of a methodology for complex driving scenario generation on urban intersections
- Synthetic generation and simulation of driving scenarios on inter-sections based on real-world data
- Validation of the developed methodology
- Good English or German language skills
- Reliability, commitment, and enjoyment of working independently as well as methodically
- Experience with python
Note: Please attach brief resume and grade summary.