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Job Description
Company:
Arriver Software AB
Job Area:
Interns Group, Interns Group > Interim Intern
General Summary:
Learning Graph Representations for Lane Connections
Background
Understanding the connection relationships of lanes in a road scene is of utmost importance for autonomous driving applications. These connections are naturally represented using a graph that connects possible driving paths. The learning of such graphs is challenging due to the uncertainty in both the positions of nodes and which nodes are connected.
Purpose
This master thesis aims to explore and develop machine learning methods for predicting graph representations of lane connections in road scene data based on vehicle sensory input. The focus will be on end-to-end and possibly generative methods.
Learning Objectives
Understand more on how traffic knowledge can be abstracted beyond conventional perception tasks - the connection relationship of lanes, and possibly also the assignment relationship between lanes and traffic elements.
Student Background
Master studies, experience with python, Deep Learning, and Computer Vision.
Thesis work suitable for 1-2 students.
Location: Linköping
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