Master Thesis - Development of Multi-Model Perception System for Road Topography Estimation (m/f/d)
Our CARIDIANS are developing vehicle functions such as driver assistance systems, a next-generation infotainment platform, power electronics and charging technology, and digital services in and around the vehicle.
Our software can already be found in Volkswagen ID. models and will soon power Audi and Porsche vehicles with the E3 1.2 platform in 2024.
It's no easy task, but with experts like you, we can shape the future of mobility. Join us at CARIAD and be part of this exciting journey!
YOUR TEAM
Our "Chassis" team is seeking a Master thesis student to collaborate with a PhD student on developing an innovative algorithm for predicting road topography using monocular vision. The core research question is: How accurately can texture-less road surfaces be predicted using monocular vision?
As part of our team, you'll be involved in cutting-edge research, helping to advance powertrain, chassis, and charging functions on a high-performance computing platform. Our applications focus on driving dynamics, optimized charging, and predictive maintenance.A key research area includes controlling active damping systems based on perceived road topography to maximize driver comfort through adaptive control mechanisms.
This thesis offers a hands-on opportunity to build and test your own system using LiDAR and a camera, allowing you to evaluate the algorithm in real-world applications. You will also delve into hardware system development, explore modern depth estimation techniques, and investigate machine learning approaches.
Join us and gain invaluable experience in the field of automotive technology!
WHAT YOU WILL DO- Conduct literature review on state-of-the-art depth estimation methods
- Design and implement selected algorithms
- Develop an integrated hardware system using LiDAR and a camera to test the algorithm in a vehicle
- Analyze and evaluate the algorithms using real-world data
- Document and present findings for your final thesis
- Prepare and present the results in a scientific pape
- Enrolled in a Master's program in Mechatronics, Computer Science, Electrical Engineering, or a related field
- Strong programming skills in Python, with experience in libraries such as OpenCV and NumPy; familiarity with TensorFlow or PyTorch is a plus
- Understanding of computer vision and deep learning concepts; knowledge of advanced architectures (e.g., transformers) is plus
- Hands-on experience with data processing and algorithm development
- Team-oriented, communicative, and proactive in problem-solving
- Interest in hardware integration and automotive engineering is a plus
- Remote work options
- Duration: 6 months
- 35 hour working week
- If you have further questions about the candidate journey at CARIAD, please contact us: careers@cariad.technology
YOUR RECRUITING CONTACT
Carmen Dörwald