30 credits - Motion Planning for Self-Driving Vehicles

Sodertalje, Stockholm
den 4 oktober 2019
den 26 november 2019
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At the department of Autonomous Transport Systems (ATS) , we develop a full stack prototype system for sustainable autonomous driving. The work is done in close cooperation with Volkswagen group, leading technology suppliers and academic institutions. We take responsibility for creating a better and greener environment by finding answers to the questions on how we can achieve radically reduced emissions, manage the negative effects of ongoing urbanization and save human lives in traffic.

A master thesis project at ATS research, is a great opportunity to work on the forefront of autonomous vehicle technologies, that help to enable automated driving in complex driving environments. Many of our current employees have started their careers with a thesis project. We also have plenty of opportunities for both PhD and expat positions.

An autonomous vehicle can be roughly divided into three major modules, Sensing, Planning, and Control.

The Sensing module deals with understanding the surroundings of the vehicle, making use of cameras, radars, lidars, etc.

The Planning module takes the information obtained by the sensing module, and plans a sequence of motions that take the vehicle to a goal destination, while avoiding collisions with other vehicles and obstacles.

The Control module receives a planned motion from the planning module, and actuates the vehicle engine and steering to ensure that the vehicle performs the planned motion.

This thesis focuses on algorithm development for the Planning module. Example videos of Planning algorithms can be found in


Thesis 1: Planning for Bus Stop Maneuvers: The goal of this thesis is to explore motion planning algorithms that tackle the particular problem of approaching and departing from a bus stop, an important scenario that creates new challenges when compared with most of the research in this field that is more focused on passenger vehicles and ignores the challenges associated with buses.

In particular, when approaching a bus stop, the motion planner is faced with three main challenges:

- Deciding the position where to stop;

- Considering the wheels of the vehicle when collision checking;

- Ensuring feasibility of the departure maneuver.

These and possibly other challenges are expected to be addressed in this thesis. Depending on the level of ambition of the thesis, simulations and/or real tests are expected, showcasing the developed methods.

Thesis 2: Flexible: We are open for suggestions of topics that the student might be interest in. This can be discussed via e-mail.

Education and skills:
Master student (Civilingenjör) in computer science, mathematics, physics or similar, preferably with specialization in numerical optimization, artificial intelligence, and robotics.
Documented experience and skills in MATLAB and C++ is a plus.
The work will be carried out at our offices in Södertälje. A thesis project is a great way to learn more about Scania and our many interesting career opportunities.

Start date: Autumn 2019 or Spring 2020 (flexible)
Estimated time needed: 20 weeks, Full-time
Language of work: Good knowledge in English is required

Contact persons and supervisors
Rui Oliveira, Supervisor,

Mahbub Kamaly, Supervisor,

Enclose CV, personal letter, and grades.

Scania is a world-leading provider of transport solutions. Together with our partners and customers we are driving the shift towards a sustainable transport system. In 2018, we delivered 88,000 trucks, 8,500 buses as well as 12,800 industrial and marine engines to our customers. Net sales totalled to over SEK 137 billion, of which about 20 percent were services-related. Founded in 1891, Scania now operates in more than 100 countries and employs some 52,000 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 centres in Africa, Asia and Eurasia. Scania is part of TRATON SE. For more information visit:

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