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Scania Södertälje Heltid

30 hp – Generating higher temporal resolution geospatial data using deep neural networks

About the job

Background

Scania is one of the world’s leading manufacturer of trucks and buses for heavy transports, as well as industrial and marine engines. Transport services and logistics services make up an increasing part of our business, which guarantees Scania’s customers cost-efficient transport solutions and high availability. Over a million Scania vehicles are in active use, in over 100 countries.

In the Connectivity section within Scania R&D, we develop new solutions for connected vehicles in our Internet of Things (IoT) platform, as part of Scania’s increasing focus on communication, services and smart transport solutions. Advanced data analysis capabilities are a cornerstone enabler in this development.

Target/scope

The goal of this project is use deep learning methods to generate intermediate geospatial data points to increase the temporal resolution (data frequency). Generative adversarial network (GAN) and super resolution GAN (SRGAN) have shown to be able to create higher resolution images from its original. We would like to explore this using our geospatial data on the temporal dimension. Today, Scania collects geopositions of its vehicles in varying frequencies and increasing the temporal resolution would allow us to improve our research and analysis such as electrification. A big challenge with geospatial data is that the position has a physical context, meaning that a simple interpolated intermediate point could yield a point in a lake, which is not desirable.

Training data is available in the form of several years of geospatial traces from Scania’s global connected fleet of more than 450 000 heavy trucks and buses. The student will have access to Scania’s Hadoop cluster as computational resources to train and run their models.

Description of the assignment

  1. Evaluate suitable metrics to compare the generated data against real data.
  2. Identify and evaluate suitable methods for generating higher temporal resolution geospatial traces.
  3. Visually compare generated data with real data on a map

If time permits,

  • Generate higher temporal resolution vehicle data in conjunction with the geospatial data.

Education/line/direction

Assign education, line or direction: masters programmes in Machine Learning, Data Science, Computer Science, Engineering Physics, Engineering Mathematics, Media Technology, or similar

Number of students: 1-2

Start date for the Thesis project: Anytime during fall 2021 or spring 2022

Estimated timescale: 20 weeks

Contact person and supervisor

Kuo-Yun Liang, data scientist, 08-553 508 33, kuo-yun.liang@scania.com

Katarina Prytz, group manager, 08-553 723 08, katarina.prytz@scania.com

Application

Your application should contain CV, personal letter and copies of grades.

Om företaget

Scania is a world-leading provider of transport solutions, including trucks and buses for heavy transport applications combined with an extensive product-related service offering. Scania offers vehicle financing, insurance and rental services to enable our customers to focus on their core business. Scania is also a leading provider of industrial and marine engines. In 2019, we delivered 91,700 trucks, 7,800 buses as well as 10,200 industrial and marine engines to our customers. Net sales totalled to over SEK 152 billion, of which about 20 percent were services-related. Founded in 1891, Scania now operates in more than 100 countries and employs some 51,000 people. Research and development are concentrated in Sweden, with branches in Brazil and India. Production takes place in Europe, Latin America and Asia, with regional production centers in Africa, Asia and Eurasia. Scania is part of TRATON SE.

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