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Thesis Work - Machine learning for driver behavior classification

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! 

 

 

At Vehicle Energy and Motion Control, our purpose is to develop and deliver the most energy efficient and situation adopted ride and drive experience. Evaluating driver input, surrounding information (e.g. camera, radar and route data) and Autonomous Drive request, as well as vehicle state sensor input, our software (mainly inhouse developed) coordinates the different actuators (engine, motors, brakes, steering) to fulfill our purpose and balance vehicle attributes. 

 

Background 
The remaining range of an electric vehicle is the distance the vehicle can travel under current conditions before the vehicle needs to be charged. Range anxiety, the fear that the vehicle will come to an unplanned stop along the route, is one of the factors slowing down the transition to battery electric vehicles. One way to reduce range anxiety is to provide the driver with reliable and accurate estimation of the remaining range. To estimate this accurately the conditions ahead of the vehicle, as well as the driver behavior needs to be estimated. The conditions ahead of the vehicle can often be estimated using navigation system data. The driver behavior is typically modeled using measurements of past energy consumption and assume that the future energy consumption will be similar. In this approach the amount of history used has a direct impact on the behavior of the range number; too much history will make the system slow to adapt to changes in driver behavior, whereas too little history will lead to a range number that changes rapidly. Further a general problem with this approach is the ability to extrapolate, e.g. if the driver spends most of the time driving in a rural environment, how to estimate the consumption in an urban environment? 
 

Scope 
The aim of this master thesis is to use big data to identify the number of consumption classes there are among drivers and design a real time energy consumption model that continuously estimates which consumption class the current driver belongs to. The consumption models to each class would be tuned offline using big data and used online based on the current driver classification. The benefits of this approach are believed to be a faster update to a change in driver behavior and more robust extrapolation. 
 

Profile 
· Since the project is part of a PhD research project in collaboration with Chalmers, at least one student should be from Chalmers University of technology. 
· Data science/engineering physics/mathematics/electrical, M.Sc., or similar. 
· Knowledge in statistics, machine learning, artificial intelligence. 
· Knowledge in MATLAB. Knowledge in C and Python programming is meritorious. 
· Analytical and independent 

· Driver’s license (B) is a merit. 

 

Application 
· Your application should include a CV, cover letter and transcript of courses and grades 
· Selection will be ongoing during the application period. We want your application as soon as possible, but no later than 2020-11-29. Please note that due to GDPR, we can't accept applications via email.

 

Duration 
· 20 weeks / 30 ECTS. 
· Starting date: January-February 2021 (Flexible). Estimated end date: Summer 2021 
· Number of students: 1 or 2 
· The work will be performed at the department of Vehicle Energy & Motion Control and the group of Energy Efficiency, Volvo Cars Corporation, Göteborg 
Contact Supervisor: Niklas Åkerblom, (niklas.akerblom@volvocars.com) 
Manager: Ole-Fredrik Dunderberg (ole-fredrik.dunderberg@volvocars.com)

 

Who are we?

Everything we do starts with people. Our purpose is to provide freedom to move, in a personal, sustainable and safe way. We are committed to simplifying our customers’ lives by offering better technology solutions that improve their impact on the world and bringing the most advanced mobility innovations to protect them, their loved ones and the people around them. 


Volvo Cars’ continued success is the result of a collaborative, diverse, and inclusive working environment. The people of Volvo Cars are committed to making a difference in our world. Today, we are one of the most well-known and respected car brands, with over 40,000 employees across the globe. We believe in bringing out the best in each other and harnessing the true power of people. At Volvo Cars your career is designed around your talents and aspirations so you can reach your full potential. Join us on a journey of a lifetime as we create safety, autonomous driving and electrification technologies of tomorrow.

Gothenburg, Sweden

Job requisition ID:  47547
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