In recent years large advances have been made in the field of machine learning, driven by novel deep learning methods. Deep learning is a research field that focusses on creating neural networks. This field has seen a rapid advance due to an increase in computational power, availability of large amounts of data and a wide variety of novel methods that allows for more efficient training of neural networks. Deep learning has been applied in various fields to solve many different tasks. Effective training of these neural networks requires selecting the right data, network architecture and learning method. However, thorough understanding of the task for which the neural network is trained is needed to adhere to these requirements. This thesis will illustrate that deep learning methods can effectively be applied to perception tasks by thorough understanding of the task.@en

Safe and comfortable path planning in a dynamic urban environment is essential to an autonomous vehicle. This requires the future trajectories of all other road users in the environment of the vehicle. These trajectories are predicted through modeling the motion and behaviour of these road users. In this work we state that for efficient trajectory prediction only motion indicators are not sufficient. Therefore, we propose using a curvilinear coordinate system with curvature as road infrastructure indicators to improve motion modeling and trajectory prediction. With experiments, we show that the curvilinear coordinate system with curvature sufficiently incorporates the road structure. Furthermore, we show that a sequence-tosequence RNN model is suitable to incorporate road curvature indicators directly into the modeling and prediction.

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A Twin-to-Twin Transfusion Syndrome (TTTS) is a condition that occurs in about 10% of pregnancies involving monochorionic twins. This complication can be treated with fetoscopic laser coagulation. The procedure could greatly benefit from panorama reconstruction to gain an overview of the placenta. In previous work we investigated which steps could improve the reconstruction performance for an in-vivo setting. In this work we improved this registration by proposing a stable region detection method as well as extracting matchable features based on a deep-learning approach. Finally, we extracted a measure for the image registration quality and the visibility condition. With experiments we show that the image registration performance is increased and more constant. Using these methods a system can be developed that supports the surgeon during the surgery, by giving feedback and providing a more complete overview of the placenta.@en

Twin-to-Twin Transfusion Syndrome (TTTS) is a condition that occurs in about 10% of pregnancies involving monochorionic twins. This complication can be treated with fetoscopic laser coagulation. The procedure could greatly benefit from panorama reconstruction to gain an overview of the placenta. Current state-of-the-art methods focus on panorama reconstruction in an ex-vivo setting. However, these methods fail in the in-vivo surgical setting. This paper describes the panorama reconstruction approach, the challenges posed by the in-vivo setting and the influence of these challenges on the panorama reconstruction. With experiments we show that the viewing quality is greatly reduced and that the limited motion of the fetoscope complicates and limits the precision of the image registration. We also identify the aspect necessary to shift from ex-vivo to in-vivo panorama reconstruction. Following our recommendations it should be possible to develop an approach that can be applied to TTTS surgery@en

Over a million fatal accidents occur every year with road vehicles. Road user detection for Advanced Driver Assistance Systems and Autonomous Vehicles could significantly reduce the number of accidents. Despite the research focus on road user detection and such systems, there is a surprising lack of research in real-world applications. In this work, radar and camera data are combined on an autonomous shuttle called `WEpod', driving on the public road in Wageningen, The Netherlands. With experiments we show that our method reduces the candidate region margin to 0.2m and reduces the miss rate significantly. Furthermore, our specifically trained Convolutional Neural Network improves the performance by 1.4% over vision-based road user detection, and combined with radars we improve by 7.6%. Finally, with our approach we show a performance of 95.1% on the WEpod while driving on the public road.@en