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Master Thesis - Road geometry uncertainty estimate

Background 

A vital part of several ADAS features is the road geometry estimation. The road geometry for ADAS systems is typically done by utilising lane markers, target vehicles and map data. When the sensor data is conflicting - and even when it is agreeing - there is a need to understand the uncertainty of the sensor data to adapt the noise correctly in the filters and to be able to estimate the uncertainty of the complete road geometry filter.

Project Description

In this master thesis project, you will focus on: 

  • Large scale data with ground truth tools available
  • Estimate uncertainties of sensor data by evaluating against ground truth
  • Estimate uncertainties of complete road geometry filter

Qualifications 

We are looking for two students, preferably with strong skills in:

  • Programming and scripting (C/C++, Matlab, Python)
  • Mathematics and statistical modelling
  • Signal processing

Further information

Final application date: 2019-11-30. Please send in individual applications with CV, motivational letter and grade transcripts. If you wish to partner with someone, simply note that in your application. 

Planned start: 2020-02-01, with some flexibility. 

Duration: 30 ECTS 

For questions regarding the project, please Contact: jonatan.silvlin@zenuity.com 

Or, know someone who would be a perfect fit? Let them know!

Gothenburg, Sweden

Lindholmspiren 2
417 56 Gothenburg, Sweden Directions recruitment@zenuity.com

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