Company Description
As an automotive supplier, DENSO is leading in developing and providing components and systems for heating, air conditioning, motor cooling, exhaust gas aftertreatment, automotive electrics and electronics and instrumentation.
Job Description
Complexity and scale of automotive systems are increasing, particularly in the area of automated driving and advanced driving assistance systems. The increased complexity of such safety-critical systems demands for advanced testing and verification methods to ensure safety under an abundance of operating conditions and traffic scenarios that may be encountered.
Recently, generative AI has shown promising results for content generation, such as for text (ChatGPT), image (stable diffusion), or video generation (OpenAI SORA). In our R&D work, we would like to explore the possibilities of using generative AI to create critical traffic scenarios learned from traffic data.
We are seeking for a talented and driven Working Student / Master thesis student (m/f/d) to join the Systems Engineering R&D team and support the development of testing methods by using recent results from generative AI for trajectory and scenario generation. In your role, you gain hands-on experience on the training and validation of generative AI models, and contribute to our R&D testing platform for automated driving. The work is part of the public funded project nxtaim.de.

QUALIFICATIONS

• Pursuing a B.Sc/M.Sc degree in Computer Science, Electrical Engineering, or a related field.
• Strong programming skills in Python.
• Familiarity with machine learning training frameworks (e.g. pytorch) is a big plus.
• Familiarity with optimization / dynamical systems / control engineering is a plus.
• Fluent level of English or German is required.
• Team player with intercultural competencies as well as independent working style with good self-organization capabilities.

• Adaptation of a diffusion model for traffic trajectory/scenario generation
• Develop criticality metrics for traffic scenarios
• Develop optimization-based techniques to guide the generation towards critical scenarios
• Integration of the methods into our existing testing workflow
• Evaluate the testing campaign to extract critical test case