What will you be doing?
Recent innovations, such as automated driving and smart mobility, have elevated the safety-criticality of automotive systems due to the impact of these technologies on the traffic behavior and safety. New safety validation and assessment methodologies are required to provide the level of assurance that matches the societal impact of these systems. Scenario-based safety validation for automated (and autonomous) driving is one of the proposed approaches that is broadly supported by the automotive community. Risk assessment is an essential component of the scenario-based safety validation as it indicates the acceptance criteria of the AD system. The ISO26262 standard, state-of-the-art in automotive functional safety, provides guidelines for assessing the risk of hazardous events. However, this is done by a qualitative judgments of ‘experts’. A wrong judgment can lead to an unsafe feature of the automotive system (if the risk is rated too low) or to an unnecessarily expensive design process (if the risk is rated too high). This clearly shows the need for a quantitative measure for the risk.
In this project, you will apply data-driven algorithms to quantify the actual risk of a driving scenario. The objective of this research is to find one or more quantitative measures for the risk, given certain scenarios and an automated driving system. The metric(s) need(s) to be data driven, such that the resulting measure(s) is/are not affected by (wrong) judgments from ‘experts’. Three different aspects of risk will be assessed, which are the exposure (how often does a certain scenario occur), the severity (the extent of harm to one or more individuals that can occur given the specified scenario), and the controllability (given that the system is subject to the scenario, how likely is it that the system can avoid an accident). Using real data, a case study needs to be performed to quantify the risk. This will require a statistical analysis of the data and (virtual) simulations of a system.
Your activities are:
- Literature study to overview similar problems in other fields of research.
- Determine several metrics that can be used to quantify the risk and its constituents, i.e., exposure, severity, and controllability.
- The (dis)advantages and assumptions that are used should be described.
- Apply the metrics on a real dataset and a real automated driving system.
What do we require of you?
- Good communication in English
- Experience with Python, MATLAB or similar software is preferred
- Not afraid of mathematics
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 vehicles and developed methods for testing automated 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 or she will help you to get acquainted with the department and give you guidelines for your research 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. Click here
to find more info about internships at TNO.
Has this vacancy sparked your interest?
Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.
Note that applications via email and third party applications are not taken into consideration.
Contact: Erwin Gelder, de
Phone number: +31 (0)88-86 65674