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

Minor and trace elements in sediment sampled across the Permian-Triassic boundary exposed at Festningen, Spitzbergen

The data table lists the concentrations of 44 minor and trace elements in 124 samples of sediment, collected approximately every few centimeters in a continuous profile across the Permian-Triassic boundary exposed at Festningen, Spitzbergen. The age of these sediments is approximately 251 Ma, and the period of time represented within the sediment profile is about 1 million years. Trace element concentrations in untreated bulk sediment samples were measured using quadrupole inductively coupled plasma mass spectrometry. The purpose of the study was to investigate how trace element concentrations of sediments change across the Late Permian Extinction and the Permian – Triassic Boundary, and to evaluate trace element concentrations as a proxy for massive volcanism in Siberia at that time

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