Type dienstverband:
Internship and graduation project
Uren per week:
Fulltime – 40

Internship | Unstructured Environment Model Predictive Control (MPC)

About this position

Are you interested in a TNO-master thesis at the Automotive Campus, willing to work on Model Predictive Control (MPC) motion planning algorithms to improve traffic safety in closed environments? Please read further.

What will be your role?

Automated vehicles (AV) have become widely popular in recent years since they have the potential to bring several benefits such as increased road safety, traffic throughput and fuel efficiency [1]–[3]. In order to utilize the full potential of this increased level of autonomy, AVs have to be able to function in different operational domains, including highways, urban areas, parking garages and closed environments. For the latter two domains, the environment might be unstructured and difficult to be described continuously, and might be best represented by a discrete representation (e.g. occupancy grid maps) or a hybrid combination of both. Furthermore, urban road layout (roundabouts, intersections) might be difficult to represent by analytical, continuously differentiable functions. To be able to navigate on such unstructured, discontinuous and/or complex environments, motion planners have to be created to allow for calculation of safe and comfortable trajectories given discrete inputs.
Within TNO, currently a motion planner is developed following a Model Predictive Control (MPC) algorithm (Ploeg, Smit, Teerhuis, & Silvas, 2022). To describe the road layout, continuously differentiable polynomial descriptions of the lane markings are fed into this motion planner. However, to improve the planner performance on complex road layouts, as well as unstructured environments, it should be adapted to be able to handle discrete inputs.

During this assignment you will be investigating the following research questions

  • What types of algorithms exists that perform MPC-based trajectory planning taking in discrete inputs (occupancy grid maps, discrete lane representations)?
  • How to develop/integrate algorithms that perform MPC-based trajectory planning taking in discrete inputs (occupancy grid maps, discrete lane representations)?
  • What are the benefits and shortcomings of discrete-input MPC trajectory planning?
  • Is it possible to combine continuous and discrete inputs to develop a hybrid-input MPC trajectory planner?

To answer the research questions, you will:

  • Perform a short literature review & categorize of existing discrete-input MPC algorithms that are being used for trajectory planning
  • Select, develop and/or integrate the discrete (or hybrid) -input MPC algorithm in the current TNO-IVS motion planning software pipeline
  • Perform a thorough analysis of the performance of the implemented algorithm in simulation and, if time allows, on the TNO demonstrator vehicle.
  • Give a final presentation & write a report, preferably in short article format

What we expect from you

  • Good communication in English [Required]
  • Experience with C++ [Required]
  • Experience with Model Predictive Control (MPC) algorithms [Required]
  • Experience with Robotics Operating

We like you to start as soon as possible. The thesis fits to a 9 month assignment (based on full time availability). We require you to work partially in Helmond at the TNO office to enable you to work with our tools if needed and to have short communication lines.

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. 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 you'll get in return

You want to work on the precursor of your career; a work placement 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. Naturally, we provide suitable work placement compensation.

TNO as an employer

At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. 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. Read more about TNO as an employer.

At TNO we encourage an inclusive work environment, where you can be yourself. Whatever your story and whatever unique qualities you bring to the table. It is by combining our unique strengths and perspectives that we are able to develop innovations that make a real difference in society. Want to know more? Read what steps we are taking in the area of diversity and inclusion.

The selection process

After the first CV selection, the application process will be conducted by the concerning department. TNO will provide a suitable internship agreement. If you have any questions about this vacancy, you can contact the contact person mentioned below.

Has this job opening sparked your interest?

Then we’d like to hear from you! Please contact us for more information about the job or the selection process. To apply, please upload your CV and covering letter using the ‘apply now’ button.