Master thesis - Vision Foundation Models enhanced Vision-Language Model 3D Spatiotemporal Reasoning

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Artificial Intelligence

In short

This Master's thesis project aims to enhance the spatiotemporal reasoning of Vision-Language Models for autonomous driving by integrating state-of-the-art Vision Foundation Models. The project offers hands-on experience with advanced AI architectures, access to extensive datasets and GPU infrastructure, and close collaboration with AI researchers in the automotive field.

Responsibilities

Investigate methods for enhancing spatiotemporal reasoning capabilities of VLMs by leveraging representations from state-of-the-art VFMs.
Formulate exact research questions together with the student based on current literature, the student’s interests, and the specific project direction.
Explore distillation methods to transfer spatiotemporal knowledge from VFMs to VLMs.
Investigate architectural modifications to insert or fuse VFM features into the VLM pipeline.
Evaluate the enhanced model on autonomous driving benchmarks that require complex temporal and spatial reasoning (e.g., spatial VQA, 3D perception-based tasks, driving trajectory prediction).

Requirements

Master’s student in Computer Science, Robotics, Engineering Physics, Electrical Engineering, Applied Mathematics, or a related field.
Experience with Deep learning and machine learning.
Experience with Computer vision and/or natural language processing.
Experience with PyTorch or similar frameworks.
Experience with Autonomous driving or robotics.

Benefits

The opportunity to work with state-of-the-art deep learning architectures at the intersection of vision and language.
Access to large-scale autonomous driving datasets and premium GPU computing infrastructure (e.g., A100 40-80Gb clusters).
Close collaboration with researchers working on next-generation AI for autonomous vehicles.
A chance to contribute to an active research area with the potential for scientific publication.

Skills

Machine LearningDeep LearningComputer VisionNatural Language ProcessingPyTorchAutonomous DrivingRobotics

#autonomous driving#AI#machine learning#computer vision#language models#3D reasoning#spatiotemporal reasoning#deep learning#research#Master Thesis

About Scania Group

Scania is a world-leading provider of transport solutions committed to a better tomorrow. Our purpose is to drive the shift towards a sustainable transport system. In doing so, we are creating a world of mobility that’s better for business, society and our environment. Employing more than 50,000 people in about 100 countries, Scania’s research and development is concentrated in Sweden, while production takes place in Europe and South America.

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