30 Credits - Map Matching Optimization Problem for Landmark based Localization in Autonomous Driving

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 Research, 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 requirement of autonomous driving. Landmark based Localization (LBL) is an attractive position estimation method in autonomous driving especially in public roads. In this method, at least two sources of information are used: detected landmarks around vehicle and an accurate map of the road. Landmarks can be observed using different sensors such as cameras, Lidars, and Radars. The map is built offline and consists of different types of landmarks, road edges, etc. LBL estimates the vehicle position by matching the observation to the map in three different main steps: detection of landmarks, associate the observations to the map (called map matching), and optimizing vehicle position.

Map matching step in LBL is a crucial step, because the error may propagate into the next step and leads to a significant error in optimization solution. This thesis project considers map matching step of the LBL assuming that both landmark detections and map information are available.

This thesis project will formulate the map matching step as a standard optimization problem and will investigate different techniques for solving the formulated problem.

Scope of the project

The objectives of this Master thesis project are

  • Formulate map matching optimization problem.
  • Compare different techniques to solve the map matching optimization problem.
  • Implement and evaluate a technique based on the result of comparison.
  • Time: 20 weeks

    Start date: August/September 2019

    Credits: 30 HP (ECTS)

    Qualification

  • Master student in robotics, control, automation, autonomous systems, mechatronic, mathematic, applied physics, or similar program.
  • Strong mathematical, analytical and statistic background.
  • Experience in MATLAB programming is a must, and C++ experience is a plus.
  • Contact persons and supervisors

    Mansoureh Jesmani, +46 8 553 503 59, mansoureh.jesmani@scania.com or

    Navid Mahabadi, +46 8 553 727 26, navid.mahabadi@scania.com

    Application

    Your application should include CV, cover letter and transcrips of records.

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