Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules

Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strictly, resulting in varying degrees of rule conformity. Such deviations from given rules are key components of today's road traffic. In autonomous driving, robotic agents can disturb traffic flow, when rule deviations are not taken into account. In this paper, we present an approach to derive the distribution of degrees of rule conformity from human driving data. We demonstrate our method with the Waymo Open Motion dataset and Safety Distance and Speed Limit rules.Comment: Daniel Bogdoll and Moritz Nekolla contributed equally. Accepted for publication at IV 202

Left ventricular functional assessment in murine models of ischemic and dilated cardiomyopathy using [18 F]FDG-PET: comparison with cardiac MRI and monitoring erythropoietin therapy

Background We performed an initial evaluation of non-invasive ECG-gated [18 F]FDG-positron emission tomography (FDG-PET) for serial measurements of left ventricular volumes and function in murine models of dilated (DCM) and ischemic cardiomyopathy (ICM), and then tested the effect of erythropoietin (EPO) treatment on DCM mice in a preliminary FDG-PET therapy monitoring study. Methods Mice developed DCM 8 weeks after injection with Coxsackievirus B3 (CVB3), whereas ICM was induced by ligation of the left anterior descending artery. LV volumes (EDV and ESV) and the ejection fraction (LVEF) of DCM, ICM and healthy control mice were measured by FDG-PET and compared with reference standard results obtained with 1.5 T magnetic resonance imaging (MRI). In the subsequent monitoring study, LVEF of DCM mice was evaluated by FDG-PET at baseline, and after 4 weeks of treatment, with EPO or saline. Results LV volumes and the LVEF as measured by FDG-PET correlated significantly with the MRI results. These correlations were higher in healthy and DCM mice than in ICM mice, in which LVEF measurements were somewhat compromised by absence of FDG uptake in the area of infarction. LV volumes (EDV and ESV) were systematically underestimated by FDG-PET, with net bias such that LVEF measurements in both models of heart disease exceeded by 15% to 20% results obtained by MRI. In our subsequent monitoring study of DCM mice, we found a significant decrease of LVEF in the EPO group, but not in the saline-treated mice. Moreover, LVEF in the EPO and saline mice significantly correlated with histological scores of fibrosis. Conclusions LVEF estimated by ECG-gated FDG-PET significantly correlated with the reference standard MRI, most notably in healthy mice and mice with DCM. FDG-PET served for longitudinal monitoring of effects of EPO treatment in DCM mice

[68Ga]-albumin-PET in the monitoring of left ventricular function in murine models of ischemic and dilated cardiomyopathy: Comparison with cardiac MRI

<b>Purpose</b>\ud \ud The purpose of this study is to evaluate left ventricular functional parameters in healthy mice and in different murine models of cardiomyopathy with the novel blood pool (BP) positron emission tomography (PET) tracer [<small>⁶⁸</small>Ga]-albumin.\ud Procedures\ud \ud ECG-gated microPET examinations were obtained in healthy mice, and mice with dilative (DCM) and ischemic cardiomyopathy (ICM) using the novel BP tracer [<small>⁶⁸</small>Ga]-albumin (Alb<small>_BP</small>), as well as [<small>¹⁸</small>F]-FDG microPET. Cine-magnetic resonance imaging (MRI) examination performed on a clinical 1.5-T MRI provided the reference standard measurements.\ud Results\ud \ud When considering the combined group of healthy controls, DCM and ICM <small>_BP</small>-PET significantly overestimated the magnitudes of EDV (<small>_BP</small>, 181 ± 86 μl; cine-MRI, 125 ± 80 μl; P < 0.001) and ESV (<small>_BP</small>, 136 ± 92 μl; cine-MRI, 96 ± 77 μl; P < 0.001), whereas the EF (<small>_BP</small>, 31 ± 16 %; cine-MRI, 33 ± 21 %; P = 0.910) matched closely to cine-MRI results, as did findings with [<small>¹⁸</small>F]-FDG. High correlations were found between the measured cardiac parameters (EDV: R = 0.978, ESV: R = 0.989, and LVEF: R = 0.992).\ud Conclusions\ud \ud Measuring left ventricular function in mice with [<small>⁶⁸</small>Ga]-albumin BP PET is feasible and showed a high correlation compared to cine-MRI, which was used as a reference standard

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Comment: 93 page