PhD student position in autonomous systems at the Division of Vehicular Systems. Department of Electrical Engineering, Linköping University.

Your work assignments

Machine learning and optimal control are core techniques to enable an autonomous system to plan and act in dynamic and uncertain environments where actors enter and leave the environment dynamically and where the behavior of surrounding actors is uncertain. The development of theory, models, and methods to solve this problem contains major research challenges. This is a very active field of research, driven also by the development of autonomous vehicles that need to act in environments with different levels of complexity.

In this project we research new methods in machine learning and optimal control, and how they can be combined to generate robust and resilient methods for planning and control of autonomous systems. A core research question is how the behavior of the surrounding environment, e.g., pedestrians, cyclists, cars, can be predicted, and how they influence each other, and how this can be used to make probabilistic predictions of future behavior. These predictions should then be used to obtain proactive decision-making and control. The basic research and theoretical models will be evaluated in real-life datasets, e.g., in automated driving.

More information and application

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