J.F.P. (Julian) Kooij

profiel

Biografie

Julian Kooij (1982) is an Assistant Professor at the Intelligent Vehicles group, performing research and education on autonomous driving and vehicle perception. The group is part of the Cognitive Robotics (CoR) department of the 3ME Faculty.

His research interests include statistical machine learning and probabilistic inference for sensor processing (computer vision), environment understanding, and predictive models of Vulnerable Road User (VRU) behavior. Good predictive models of VRU behavior are essential for automated vehicles to guide their decision making and trajectory planning. This is especially true for the challenging urban environment, where interactions with VRUs are frequent.

Before joining 3ME, he was a PostDoc at the pattern recognition and computer vision lab of the EWI Faculty of TU Delft. In this period, he collaborated on setting up the new Technology In Motion (TIM) lab at Leiden University Medical Hospital, and developed new signal processing techniques to detect subtle tremors in patients. His PhD at the University of Amsterdam addressed automated analysis of pedestrian tracks, using probabilistic graphical models for unsupervised learning and online predictive Bayesian inference.

In 2013 he interned at Daimler AG in Ulm, Germany. There, he worked on improved pedestrian path prediction for intelligent vehicles, exploiting various contextual cues such as pedestrian head orientation and location relative to the road. During the NWO Cassandra and EU-FP7 ADABTS projects, graphical models were developed for the integration of audio-visual cues, and for anomaly detection, in surveillance applications.

Projecten

publicaties
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vakken
2016 - Intelligent Vehicles
2017 - Intelligent Vehicles
2017 - Robotics Practicals
2017 - ME-VE Literature Survey
2017 - ME-VE Internship (optional)
2018 - Robotics Practicals
2016 - 3D Robot Vision
2017 - Integration Project Vehicle Engineering
2017 - 3D Robot Vision
2018 - Intelligent Vehicles
2015 - Datamining
2018 - 3D Robot Vision
2019 - Robotics Practicals
2019 - 3D Robot Vision
2019 - Intelligent Vehicles
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2016-10-01 - 2020-01-01