Machine learning models for map element detections
Organization
Bolt
Abstract
Maps are the key ingredient for all our services at Bolt. Their data is used in many applications - be it visualizing a trip in a rider’s invoice, routing from A to B, or estimating the time of arrival for the yummy dinner of that Mexican place you ordered. It is, therefore, critical to keep our maps as up to date as possible. For this, we can use GPS tracking data of our ride hailing drivers to detect e.g. longer waiting times or uni-directional driving patterns to indicate map elements such as traffic lights or one ways.
In this project, you will research, design and test a machine learning model (e.g. LightGBM, GNN or HMM) that can detect missing map elements from our ride hailing tracking data. This could be detecting and assigning new turn restrictions, one ways, and traffic lights, or finding missing roads.
In this project, you will research, design and test a machine learning model (e.g. LightGBM, GNN or HMM) that can detect missing map elements from our ride hailing tracking data. This could be detecting and assigning new turn restrictions, one ways, and traffic lights, or finding missing roads.
Graduation Theses defence year
2022-2023
Supervisor
Sophie Laturnus
Spoken language (s)
English
Requirements for candidates
Level
Masters
Keywords
Application of contact
Name
Sophie Laturnus
Phone
E-mail