Master Thesis - Contrastive Learning for Multi-Modal Sensor Data in Autonomous Driving
Why This Matters
Contrastive learning is a powerful technique that helps networks learn by comparing positive and negative samples in an unsupervised manner. This method has been widely successful in vision tasks, enabling networks to learn with fewer annotations. However, contrastive learning in multi-modal data (such as radar, lidar, and cameras) is still relatively unexplored in the automotive industry. In this project, you’ll push the boundaries of contrastive learning, applying it to multi-modal sensor data to enable more efficient, accurate training in autonomous driving systems.
Your Role in the Project
In this master thesis project, you will:
- Conduct a literature review on contrastive learning, focusing on multi-modal data sources.
- Design a novel multi-modal self-supervised task, train a network, and evaluate its performance on relevant downstream tasks.
- Test your solution using Zenseact’s dataset, featuring real-world autonomous driving data.
- Document your findings, methods, and potential improvements to the approach.
What We’re Looking For
We are seeking two motivated students with:
- A strong background in mathematics (calculus, optimization, statistics).
- Solid programming skills (software engineering, algorithms, debugging).
- Experience with deep learning frameworks (preferably PyTorch).
- Knowledge of computer vision (feature descriptors, camera models).
- Comfort with Linux, terminal environments, and Git.
What’s in it for You?
- Opportunity to work with cutting-edge technologies in autonomous driving and sensor data processing.
- A chance to design and implement your own solution to real-world challenges in multi-modal learning.
- Mentorship from experts in the field, allowing you to grow your knowledge and skills.
- Expand your professional network by working with a global company leading in autonomous driving innovation.
How to Apply & Important Details
This project is suitable for two students, and we encourage applicants to apply with a partner. If applying with a partner, please note this in your application but submit individual applications, including your CV, motivational letter, and grade transcripts.
- Planned start: January 2025 (with flexibility).
- Final application date: November 15, 2024 (applications are reviewed on a rolling basis).
- Duration: 30 ECTS.
For more details or questions, please contact: Amer Mustajbasic (amer.mustajbasic@zenseact.com).
More about Zenseact
Our software makes a difference.
Using AI-based technology to create the ultimate driver support, we’re fighting to end car accidents and make roads safe for everyone. Around 1,4 million people die in traffic yearly while approximately 50 million people get injured. Many get disabled as a result of their injury. We can do better.
One purpose, one product.
We’re a software company dedicated to revolutionizing car safety. By designing the complete software stack for autonomous driving and advanced driver-assistance systems, we’re fighting to end car accidents and make roads safe for everyone. Zenseact was founded by Volvo Cars, and the teams are based in Gothenburg, Sweden, and Shanghai, China.When we aim for zero accidents faster, we strive to speed up the transition to safe automation. This is essentially achieved by making cars updatable – like a computer or a phone. With regular software updates, a vehicle can be made safer long after its production. By accelerating improvement loops, shortening development cycles, and deploying high-capacity software quickly, we can make cars safer, faster.
Culture with people at heart
To achieve our mission of saving lives and ending traffic accidents is to go where nobody has before. It requires us to venture into the unknown, pioneering new technology and pushing the frontier of autonomous driving. While there’s no denying our determination and expertise, we must stand united to succeed. By fostering a culture of support and enablement – a place of psychological safety where all of us can thrive – everything else will follow. We call this a people-at-heart culture. This culture means caring. It means the company cares about me, and we care about one another. It means sharing, so we give each other energy and have fun together. Our culture is also about belonging. It’s important to feel at home and that we can be ourselves at work. Finally, a people-at-heart culture means well-being. So, we enjoy the flexibility needed to be and do our best – at work and in life.
Zenseact works proactively to create a culture of diversity and inclusion, where individual differences are appreciated and respected. To drive innovation we see diversity as an asset, which means we value and respect differences in gender, race, ethnicity, religion or other belief, disability, sexual orientation or age etc.
Interviews are held on a continuous basis, so we highly recommend that you submit your application at your earliest convenience.
- Competence area
- Opportunities for Students, Graduates & Innovators
- Locations
- Gothenburg, Sweden
- Remote status
- Hybrid
Gothenburg, Sweden
About Zenseact
One purpose, one product
We are a software company focused on transforming car safety. By developing a complete software stack for autonomous driving and advanced driver-assistance systems, we aim to eliminate car accidents and make roads safer for all. Founded by Volvo Cars, Zenseact operates globally, with teams in Gothenburg and Lund, Sweden; Munich, Germany; and Shanghai, China.
Master Thesis - Contrastive Learning for Multi-Modal Sensor Data in Autonomous Driving
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