30 Credits - Location Finder in HD Map i Sodertalje

Scania is now undergoing a transformation from being a supplier of trucks, buses and engines to a supplier of complete and sustainable transport solutions. Autonomous vehicle development at Scania is advancing at a very high pace and self-driving trucks and buses on public roads will soon see the light of day.

Thesis project at Scania is an excellent way of making contacts for your future working life. Many of our current employees started their career with a thesis project.

Background
Autonomous Transport Solutions (ATS) Research is responsible for developing, testing and piloting future frontier ATS concepts. This work is done using agile and self-steered teams with the ambition to detect and evaluate upcoming technologies, and prepare these for industrialization. We work in close cooperation with Volkswagen Group Innovation, leading technology suppliers and academic institutions.

One of the groups within the research department, EARP has the strategic and operational responsibility for the research of next generation Perception and Localization solutions for the ATS environment.

In this role, you will have the unique opportunity to be part of setting the future of Scania’s innovative Autonomous Transport Solutions. You will be part of a highly competent multicultural team instrumental in developing cutting-edge autonomous technologies where your ideas will be encouraged and embraced.

Project description
An accurate estimation of vehicle position is one of the fundamental requirements of autonomous driving. Landmark based Localization (LBL) is the state-of-the-art method in positioning of autonomous driving especially in public roads. Imagine yourself in a city center, by observing surrounding landmarks, sensor measurements and using the provided map you can find your position in the map.

When you start the vehicle at the beginning, you do not have any history of your position, which makes the problem even more challenging. In this case, we can use our observations and GPS to find the accurate vehicle position. The focus of this project is to address this problem.

In LBL two sources of information are used: landmarks observations (such as traffic signs, building corners, dashed lines, road edge, etc.) together with other measurements (GPS, odometry, etc.) and an accurate high definition map of the road. Landmarks can be observed using different sensors modalities such as cameras, Lidars, and Radars. The map is built offline and consists of different types of landmarks.

The goal of this project is to associate the detected landmarks to their corresponding map landmarks to obtain the position of the vehicle in the map “when starting the vehicle”. Observed landmarks, a high fidelity map and other necessary measurements will be provided by driving in different scenarios and data logging.

Scope of the project
The objectives of this Master thesis project are

• Study the relevant methods and techniques
• Develop an algorithm for landmark association problem, when starting the vehicle.
• Implement and prototype the algorithm
• Evaluate the algorithm using different set of real data.

Duration: 20 weeks
Start: September 2019
Credits: 30 HP (ECTS)

Qualification

• Master student in robotics, control, automation, autonomous systems, mechatronic, mathematic, applied physics, or similar programs.
• Strong mathematical, analytical and statistic background.
• Experience in MATLAB programming is a must, and C++ and Python experience is a plus.
• Experience with machine learning and artificial intelligence techniques is a merit.

Contact persons and supervisors
Mansoureh Jesmani | +46 8 553 503 59 |
Navid Mahabadi      | +46 8 553 727 26 |