Master Thesis - A framework for smart vehicular data collection i Göteborg

Thesis Worker at Volvo Cars

Welcome to explore the world of Volvo Cars by writing your thesis with us! As a thesis worker in our organization you are supported by a supervisor who follows you during your project. All thesis projects are arranged in business critical areas and therefore you will be able to contribute to our company purpose – providing freedom to move in a safe, sustainable and personal way – from day one! 

Background 
In modern smart vehicles, trigger-based data collection is used to sample and store/transmit data only upon certain events. For instance, to gather the speed at which a certain vehicle is moving if such a vehicle moves at more than 120 Km/h. Trigger-based data collection routines can be divided into two main families: those referred to as stateless, that can decide on whether to sample an event based on the event itself (e.g., checking if a speed report is greater than 120), and those referred to as stateful, that need to jointly assess multiple events together to make a decision (e.g., checking if the average speed of the last three reports is greater than 120). In stateless data collection routines, data selection and data sampling coincide. In stateful data collection, more complex routines are required to first aggregate the data, and then sample the relevant one. 

Scope 
In this abovementioned context, data analysis systems that provide provenance (i.e., that connect analysis outcomes to the raw data that contributes to each outcome) and also allow for distributed data analysis can alleviate the definition of stateful data collection routines, letting analysts define how the aggregated data should look while automating its collection. Furthermore, they can also enable efficient, high-throughput, and low-latency data analysis through their analysis APIs. Based on these observations, this thesis intends to explore the feasibility of using state-of-the-art provenance tools in stream processing to enable stateful data collection routines at Volvo. The candidates can count on state-of-the-art tools that have been jointly developed by Volvo and Chalmers, and supervision from the researchers behind such tools.

Use cases include:
-    Selective sampling (What data do we need to store?)
-    Data traceability (What is the data lineage?)
-    Application debugging (How to find out when something is wrong?)

Your contribution is our future
Computer Science or related programs 
Classes in distributed systems, or related ones 
Experience in Java or C++ 

Duration 
•    The Thesis works starts in January 2023 or earlier if possible.
•    30 ECTS (academic credits) if in agreement with your Thesis Advisor in University
•    This thesis is suitable for 1- 2 students
 
Be part of the change – apply today!
Please note that due to GDPR, applications via email will not be accepted.
 
Please apply no later than November 28th, 2022.
 
For questions about the thesis work, please contact supervisor Bastian Havers at bastian.havers@volvocars.com (+46 709613871) or technical specialist Krister Johansson at krister.johansson@volvocars.com +46 723716193).

We are Volvo Cars 
Volvo Cars is a company on a mission to bring traditional car manufacturing into a connected, sustainable and smart future. Since 1927, we've been known for our commitment to safety and for creating innovative cars that make life less complicated. In 2010, we decided to radically redefine our business, resulting in a totally new generation of cars and technology, as well as steady growth and record sales - and we're only getting started. By 2025, we aim to have yearly sales of 1.2 million cars, 50% of which will be electric vehicles and sold directly to customers through digital channels. We also intend to strengthen our position as a modern, progressive employer committed to diversity and gender and racial equality. We believe in the power of people. Join us and be part of Volvo Cars' transformation.