Robin Chan

Robin Chan
Bielefeld University · Faculty of Technology

Doctor of Natural Sciences

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19
Publications
2,292
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119
Citations

Publications

Publications (19)
Preprint
Full-text available
Deep neural networks (DNN) have made impressive progress in the interpretation of image data, so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. From an ethical standpoint, the AI algorithm should take into account the vulnerability of objects or subjects on the street that ran...
Preprint
Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertainties. Training deep neural networks is already resource demanding and so is also their uncertainty quantification. In this overview article, we survey methods that we de...
Preprint
Full-text available
Semantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task and they are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed t...
Conference Paper
Full-text available
VRUs in a reachable area depending on the ego-car's velocity. Moreover, filtering via the degree of detection, allows for further contextualization in two regards. We measure a segmentation CNN's detection ability of well as visualization tools for the usecase of semantic segmentation in autonomous driving. Our approach present and implement method...
Preprint
Full-text available
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle previously-unseen objects. However, detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving, especially if...
Preprint
Full-text available
Deep neural networks (DNNs) for the semantic segmen-tation of images are usually trained to operate on a pre-defined closed set of object classes. This is in contrast to the "open world" setting where DNNs are envisioned to be deployed to. From a functional safety point of view, the ability to detect so-called "out-of-distribution" (OoD) samples, i...
Conference Paper
Convolutional neural networks (CNNs) have seen spectacular advances over the past century, particularly improving the state-of-the-art in computer vision tasks. Semantic segmentation, an image classification at pixel-level, is an essential step in understanding a vehicle's surroundings via camera images for autonomous driving. While CNNs keep becom...
Preprint
Full-text available
In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes. Neural networks are usually trained to reduce the overall number of errors, attaching identical loss to errors of all kinds. However, this is not necessarily aligned with human intuition. For instance, an overlooked pedestri...
Preprint
Full-text available
In recent years, deep learning methods have outper-formed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more au...
Preprint
Full-text available
Neural networks for semantic segmentation can be seen as statistical models that provide for each pixel of one image a probability distribution on predefined classes. The predicted class is then usually obtained by the maximum a-posteriori probability (MAP) which is known as Bayes rule in decision theory. From decision theory we also know that the...
Preprint
Full-text available
As part of autonomous car driving systems, semantic segmentation is an essential component to obtain a full understanding of the car's environment. One difficulty, that occurs while training neural networks for this purpose, is class imbalance of training data. Consequently, a neural network trained on unbalanced data in combination with maximum a-...
Preprint
As part of autonomous car driving systems, semantic segmentation is an essential component to obtain a full understanding of the car's environment. One difficulty, that occurs while training neural networks for this purpose, is class imbalance of training data. Consequently, a neural network trained on unbalanced data in combination with maximum a-...

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Projects

Projects (2)
Project
Methods and Measures to Safeguard AI-based Perception Functions for Automated Driving. The objective of the research project KI Absicherung is to develop an example safeguarding strategy for the use of AI functions in autonomous driving. A key activity is to make the inner workings of these AI functions more transparent. For the first time the project brings together a team of experts on AI algorithms, 3D visualisation, animation, and functional safety.