Master Thesis
vor 2 Wochen
Master Thesis - ADAS: Golden Sample Pool for Robust Predictive Safety Function Development by Virtualization and Adaptation of Real Field Data (M/F/d)
**Systems Engineering**
**Student & Talent Programs**
- Ingolstadt
At CARIAD, it’s our mission to transform automotive mobility for everyone, everywhere, making it safer, more sustainable, and more comfortable in every way. To deliver on that promise, we’re building a unified technology and software platform, including a vehicle OS and cloud platform, as well as a unified architecture. As a 100% subsidiary of the Volkswagen Group, we’re developing solutions for all of its brands, including Volkswagen, Audi, and Porsche, and will bring our software to over 40 million vehicles by 2030.
Is this an easy task? Not at all To tackle such a huge challenge, we need a great team. And that’s where you come in. You’ll join more than 5,000 CARIDIANS already working on the latest vehicle features and functions like automated driving, state-of-the-art charging technology, as well as a new digital ecosystem. Together, we’re bringing sustainable change to one of the largest companies in the world.
**YOUR TEAM**:
For the department Predictive Safety we are looking for a master thesis student for the project Warn/Brake/Evade. You will contribute to topics like Evasive Steering Support and Autonomous Emergency Steering.
The department works on ADAS functions for predictive road safety. Using e.g. visual and radar sensors for environment recognition, our functions warn the driver in critical traffic situations and support the driver performing braking and steering maneuvers. We are dealing with simulation, vehicle testing as well as conceptual work for the system design and specification.
**WHAT YOU WILL DO**:
- Evaluate recorded ego vehicle sensor data and filter relevant traffic scenarios for predictive safety function development (e.g. near-collision scenarios)
- Create corresponding virtual scenarios from the recorded real world sensor data for safety function evaluation in a closed-loop simulation environment
- Integrate an exemplary predictive safety function into the simulation environment
- Vary the scenarios and establish a golden sample pool
- Test the function performance and robustness using the scenarios in the golden sample pool
- Support concept and series function development with the achieved findings
**WHO YOU ARE**:
- Enrolled student in the area of engineering, natural sciences, computer science or equivalent
- Very good academic performance
- Specific know-how in the field of ADAS
- Scientific way of working with high level of creativity
- High motivation and passion for road safety
- Initiative, self-management and team orientation
**NICE TO KNOW**:
- Duration: 6 months
- 35-hour week
- Remote work options
**YOUR RECRUITING CONTACT**:
Sandra Neumeier
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