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Masters Thesis Project – Title: Scenario Based Threat Estimation for Collision Avoidance – Göteborg

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Website Qualcomm


Arriver Software AB

Job Area:

Interns Group, Interns Group > Interim Intern

General Summary:

Collision Avoidance Systems are designed to prevent or reduce the severity of a collision. This can be done by automated breaking and/or steering if a threat is detected.

Predicting the threat of a collision with road users and their future position is a key part of Collision Avoidance Systems, to know when an action is necessary to avoid an incident.

Currently, threat estimation is based on object detection and motion predictions. In the real-world, object detections can be uncertain due to sensor issues, occlusion or unpredicted motion behavior. This could lead to additional risks in scenarios where human drivers would react and take precautionary actions. For example, in a case where pedestrians might be occluded by a stopped bus or might arrive at a crosswalk.

The Collision Avoidance team is in this thesis interested in exploring how to estimate plausible scenarios, the possible risk they pose and what precautionary actions can be taken to mitigate the risks.


The purpose of this thesis is to investigate how to detect situations that pose an increased risk and define motion limits associated to the risks. This information can then be used by the planning system or the driver to take a precautionary action to mitigate the risk. The main input for the functionality will be sensor data in the form of objects originating from fusion of computer vision and radar. Road geometry detection from computer vision. More data is available and can be used like road signs, ego motion and map data. The main output will be limitation in allowed driving speed and lateral motion.

The thesis will contain the following:

  • Literature study on which methods are suitable to solve the problem.
  • Implement the selected strategy.
  • Evaluate how well the method can detect hazardous situations, from simulated and real-world data.

Evaluate if possible precautionary driving limits can reduce future risks if followed.

Learning Objectives:

The student(s) will bring theoretical knowledge in analytical techniques and programming skills (C++, Python). Throughout the work the student(s) will learn how to apply theoretical knowledge on a real-world problem and get a broader understanding of the challenges faced when developing ADAS features today.

Student Background:

Master studies, experience with C++ or Python. Good with some knowledge in modulation and simulation and/or signal processing.

  • References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

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