TNO
About this position
In the field of automated vehicle (AV) safety, scenarios are crucial for testing and validating driving behaviour. However, scenarios often overlap when extracted from real world driving data, leading to ambiguity in safety assessments. For instance, a cut in scenario may seamlessly transition into a car following (CF) scenario, raising questions about when and how this transition occurs. Understanding these transitions are essential for creating realistic and reliable safety assessments. This thesis aims to develop a framework for scenario extraction and transition definition using real world driving data. The research will focus on defining rules and methodologies to accurately identify and differentiate between driving scenarios, such as CF and cut in scenarios, and to understand the transition dynamics between these scenarios. The goal is to create a logical, sequential and systematic approach to scenario extraction that enhances the safety assessment of AVs.
What will be your role?
Proposed approach
Expected outcomes
What can you expect of your work situation?
You will work at the Integrated Vehicle Safety department of TNO on the Automotive Campus in Helmond. In this department people are working on developing software for automated driving vehicles. The developed software is tested in pilots and on the public road. (more info on the department: Research on integrated vehicle safety | TNO). The people are young, enthusiastic and driven. You will work in an open area, within your own team. One of our employees will be your mentor. He will help you to get acquainted with the department and give you guidelines for your research in order to help you to get the best out of it.
Related work
What we expect from you
You should have a solid background in automotive engineering, data science, machine learning, or a related field. Ideally, they should possess a good understanding of vehicle dynamics, autonomous driving systems, and safety assessment frameworks. Proficiency in programming languages (e.g., Python, MATLAB) with experience in data mining and machine learning techniques is required. Familiarity with driving datasets and simulation tools (e.g., ROS, SUMO) is a plus. We would like you to start as soon as possible. The thesis fits to a 9-month project (based on full time availability). Finally, successful candidates should be able to spend at least 1-2 days a week in our TNO Helmond premises to enable you to work with our tools and to have short communication lines.
What you’ll get in return
You want an internship opportunity on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:
