Segmentation-guided Domain Adaptation for Efficient Depth Completion

Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks. A major problem for LiDAR-based depth completion is the inefficient utilization of convolutions due to the lack of coherent information as provided by the sparse nature of uncorrelated LiDAR point clouds, which often leads to complex and resource-demanding networks. The problem is reinforced by the expensive aquisition of depth data for supervised training. In this work, we propose an efficient depth completion model based on a vgg05-like CNN architecture and propose a semi-supervised domain adaptation approach to transfer knowledge from synthetic to real world data to improve data-efficiency and reduce the need for a large database. In order to boost spatial coherence, we guide the learning process using segmentations as additional source of information. The efficiency and accuracy of our approach is evaluated on the KITTI dataset. Our approach improves on previous efficient and low parameter state of the art approaches while having a noticeably lower computational footprint

Multivariate Confidence Calibration for Object Detection

Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods. The main difference to related work in the field of classifier calibration is that we also use additional information of the regression output of an object detector for calibration. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. In addition, we propose a new measure to evaluate miscalibration of object detectors. Finally, we show that our developed methods outperform state-of-the-art calibration models for the task of object detection and provides reliable confidence estimates across different locations and scales.Comment: Accepted on CVPR 2020 Workshop: "2nd Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD)

IPCo - Institut Positive Computing

Positive Computing umfasst Design, Realisierung und Bewertung von Anwendungssystemen und deren Einflüsse mit dem Ziel, Lebensqualität und Wohlbefinden von Menschen zu verbessern und sie bei der Entfaltung ihrer Potenziale zu unterstützen. Das Institut Positive Computing (IPCo) an der Hochschule Ruhr West soll dieses neue Paradigma in einem inter- und transdisziplinären Ansatz erschließen, untersuchen und umsetzen. Das Paradigma ist anwendbar auf nahezu alle Bereiche des privaten und beruflichen Lebens. Die Forschung des IPCo fokussiert zunächst jedoch auf die positive Nutzung von Informations- und Kommunikationstechnologien (IKT) für generationenübergreifende Herausforderungen. Hierzu sollen technologische Lösungen unter kontinuierlicher Einbeziehung menschlicher Bedürfnisse und sozialer Fragestellungen erarbeitet werden

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly