Internship | Road User Behaviour Prediction using Artificial Intelligence

Are you interested in an internship at TNO at the Automotive Campus? Do you want to work on automated driving vehicles? And developing machine-learning models of human behavior? This may be an opportunity for you!



Education type

university (wo)


Internship and graduation project

Hours a week

Fulltime – 40


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What will you be doing?

We are looking for a student to develop machine-learning models of human behavior for automated driving applications. Human-behavior models have many applications in automated driving, examples include maneuver prediction on the highway for increased safety of truck platooning, cyclist/pedestrian intention prediction for better performance of Automatic Emergency Braking systems, and human models for virtual testing of automated driving systems.

The common denominator of these examples is that they are all safety-critical applications, which poses high standards on all the components of the system. For machine-learning components this means a thorough understanding of the completeness of the training dataset with respect to the operation design domain in which the function will be deployed, and an accurate expression of confidence in the prediction. This internship aims to explore the benefits of a new method to tackle these problems.

The goal of this internship is to explore the method of Ambrogioni et al (2018) for human behavior prediction. By training a neural network on all possible behaviors, we can do inference on the most likely behavior given the data. Although TNO has a database of driving data, this is not nearly enough to cover all possible traffic behavior. Hence we need to resort to simulated data to train the network. The two goals of the internship are
  • Develop a method to acquire simulated training data that cover the operational design domain of the application (to be specified) as completely as possible, using traffic simulators, physical constraints or a combination of both.
  • Train a neural network on the training data and validate on real-life traffic data".
The expected results of the internship are a proof of concept of the method in the form of a demo and a report describing the results.

What do we require of you?

You have experience with machine learning libraries like pyTorch (preferred) or TensorFlow. We see experience with ROS as a benefit. We would like you to start as soon as possible. The thesis fits to a 6-month project (based on full time availability). You will work in Helmond at the TNO office a few days a week, to enable you to work with our tools and to have short communication lines.

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. 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.

What can TNO offer you?

You want to work on the precursor of your career, an internship provides you with the opportunity to take a good look at your prospective future employer. TNO also 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 internship, and be given the scope for you to exceed yourself and your expectations. We also provide suitable compensation for internships. 

Has this vacancy sparked your interest?

Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.

Contact: Jan-Pieter Paardekooper
Phone number: +31 (0)88-86 67315

Note that applications via email and third party applications are not taken into consideration.


Apply now



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