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Master Thesis - Radar detection using deep learning

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Background 

 

Autonomous driving systems and advanced driver assistance systems require a description of their surrounding environment. One of the sensors that contributes to this task is Radar. Radar pre-processing and its interface towards autonomous drive applications rely heavily on statistical signal processing methods, some of which are computationally complex and heavy to run. Autonomous drive applications should run in complex scenarios in real-time and have access to limited computational budget. Therefore, looking for tools that can accomplish better performance than previous methods while reduce the computational burden of radar processing chain and its customer applications are of high interest and relevance to the field of autonomous drive.

The purpose of this thesis is to explore new tools using machine learning and deep learning to alleviate radar customer applications of the underlying heavy processing needed to process and use radar data.

 

Project Description 

In this master thesis project, you will focus on:

  • Perform a literature study on different deep learning based methods for  radar processing.
  • Implement, train and evaluate a deep neural net that can detect objects based on radar data.
  • Document the results and lessons learned.

Qualifications 

We are looking for 2 students with an interest in deep learning for autonomous driving. The following skills would be highly valuable:

  • Python programming
  • Machine learning
  • Reading scientific papers
  • Handling large datasets

Having had deep learning related courses and some hands-on experience with these methods is a plus.

    Further information

    Please send in individual applications with CV, motivational letter and grade transcripts. 

    Planned start: January 2022, with some flexibility.

    Final application date: 30th of November 2021

    Duration: 30 ECTS 

    For questions regarding the project, please contact: maryam.fatemi@zenseact.com, erik.werner@zenseact.com

    Additional information

    • Remote status

      Flexible remote

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

    Gothenburg, Sweden

    Lindholmspiren 2
    417 56 Gothenburg, Sweden Directions View page

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