Identification of Similarities Among Scenarios to Validate Highly Automated Driving Functions

Context

Future innovations in the automotive industry are centered around automated driving and highly connected communication. To adequately test and validate these technologies, virtualization solutions are increasingly being used.
Scenario-based approaches enable systems to be tested and validated. The goal of this thesis is to identify critical scenarios based on measurement data from real and simulated drives. Subsequently, an approach will be developed that uses similarity analysis to identify a catalog of relevant scenarios.

Tasks

• You will identify critical parameters in a scenario.
• You will contribute ideas for the similarity analysis of various scenarios.
• You will implement and visualize the developed methods and ideas.
• You will be involved in projects and actively contribute to the project context.

Requirements

• Personal strengths: Demonstrating initiative and “out-of-the-box” thinking 
• Field of Study: Information Technology, Signal Processing, Computer Vision, Machine Learning 
• Experience: Python and/or MATLAB