PhD on Automated vehicles Operational Design Domain (ODD)

Research / Academic

This PhD studentship is part of the SAMEN project funded by the Netherlands Organisation for Scientific Research (NWO). The project ‘Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic (SAMEN)’ is open for 2 PhD candidate positions and 1 postdoc.   

The gradual deployment of Automated Vehicles (AV) in traffic will result in a transition period, in which vehicles with various levels of automation and Human Driven Vehicles (HDV) co-exist. As a consequence, new types of interactions will emerge between vehicles at different levels of automation. Proper understanding of how human drivers will respond to AVs, and how AVs could interact with HDVs rather than responding to them is urgent but lacking. This lack of understanding may result in unsafe and inefficient traffic situations. The unique aspect and aim of this project is its focus on understanding and modelling the interactions between human-driven and automated vehicles based on empirical data. In this project behavioural theories and models will be developed and validated for the interactions between AVs and HDVs using a hybrid approach for data collection. We will underpin the behavioural theory using empirical data that we will collect from field tests using AVs. We will use advanced interactive driving simulators to study the interactions between AVs and HDVs. We will scale up the resulting interaction models in a dedicated traffic flow simulation platform to evaluate the implications of mixed traffic on traffic flow and safety.

In this project several industrial partners, vehicle manufacturer, road authorities, and knowledge institutes are involved.

PhD position: Automated Vehicles Operational Design Domain (ODD)

The operational design domain is defined as the operating conditions under which a given driving automation system or feature thereof is specifically designed to function. To increase AVs capabilities we need to understand how to expand their operational design domain considering their interaction with the infrastructure, as well as, with other vehicles in complex traffic situations. The main objectives of this PhD position are to: (1) to develop accurate and reliable algorithms for infrastructure and traffic conditions features’ extraction, recognition and prediction using data driven approach; (2) to examine and evaluate the implications of different driving strategies and driving styles of AVs on human-drivers’ behaviour of nearby vehicles.

In this part of the project you will collect and use data from driving simulators as well as from field tests. You are expected to collaborate with vehicle manufacturers.

The duration of the PhD position will be for four years (48 months). As a PhD you will enrol in the CEG Graduate School ( and have as well the opportunity to join the TRAIL Research School ( Both platforms provide a stimulating research environment and an ample support during your PhD.

The project will commence in the Autumn of 2019.


Requirements for the PhD position:MSc in Computer science and/or applied mathematics

Skills required: good analytical skills, image-processing and computer-vision, working with big data, good communication skills, open minded, team player, and excellent English level (speaking, reading, and writing).

Salary Benefits:

TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

The minimum salary is your salary in your first year. The salary mentioned as the maximum will be your salary in your fourth year.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit for more information.

Work Hours:

38 hours per week


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