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This internship assignment focuses on Smart Seats: Predictive Vibrotactile Motion Cueing to Reduce Carsickness at TNO.
Many passengers experience motion sickness when travelling, which is a challenge, especially in automated vehicles. Finding ways to reduce motion sickness is crucial to the success of future automated vehicles. At TNO, we have already shown that giving passengers auditory or vibrotactile cues can help them anticipate motion and, as a result, feel better. Building on this, we developed a car seat demonstrator which can produce spatiotemporal vibration patterns in the seat. In this thesis, you will design, test and evaluate different spatiotemporal cueing patterns for different vehicle manoeuvres. Such a solution contributes to increasing passenger comfort in automated driving by communicating the vehicle's intentions to the passenger.
Which spatiotemporal vibration patterns provide effective and comfortable motion cues in automated vehicles?
You will use an existing motion cueing demonstrator: a seat pad fitted with 24 vibration motors across the seat pan and backrest. Each motor can be individually controlled in terms of vibration intensity and duration. This demonstrator will be connected to the static driving simulator. As part of the project, you will select relevant automated driving use cases (e.g., stop‑and‑go traffic, lane changes, or slalom manoeuvres) and test different spatiotemporal vibration patterns. The patterns you develop will include decision logic to handle overlapping or successive events and varying acceleration levels. Finally, you will evaluate which cueing strategies are most effective and comfortable for passengers. The results will contribute to motion sickness mitigation strategies in automated vehicles and help to build passenger trust by making vehicle intentions more transparent.
Proposed approach:
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 department also develops methods to assess the safety and comfort on automated vehicles. 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. S/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. Parallel to this project, a separate project is planned to implement an interface between motion planning in automated vehicles and the cueing demonstrator.
The ideal candidate for this master thesis assignment has a background in human factors research or design, is proficient in Python and wants to contribute to the improvement of (comfort in) automated vehicles. You are able to work independently, but also know when to ask for support. We expect you to be able to start as soon as possible. The assignment is designed as a 6 month project based on full-time availability. To ensure smooth collaboration and access to our tools, we require you to work at the TNO office in Helmond at least twice a week.
TNO attaches a great deal of value to your personal and professional development. You will be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:
At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world.
Innovation with purpose: that is what TNO stands for. We develop knowledge not for its own sake, but for practical application. TNO connects people and knowledge to create innovations that boost the competitive strength of industry and the well-being of society in a sustainable way.
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