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

Julian Wörmann; Daniel Bogdoll; Etienne Bührle; Han Chen; Evaristus Fuh Chuo; Kostadin Cvejoski; Ludger van Elst; Philip Gottschall; Stefan Griesche; Christian Hellert; Christian Hesels; Sebastian Houben; Tim Joseph; Niklas Keil; Johann Kelsch; Hendrik Königshof; Erwin Kraft; Leonie Kreuser; Kevin Krone; Tobias Latka; Denny Mattern; Stefan Matthes; Mohsin Munir; Moritz Nekolla; Adrian Paschke; Maximilian Alexander Pintz; Tianming Qiu; Faraz Qureishi; Syed Tahseen Raza Rizvi; Jörg Reichardt; Laura von Rueden; Stefan Rudolph; Alexander Sagel; Tobias Scholl; Gerhard Schunk; Hao Shen; Hendrik Stapelbroek; Vera Stehr; Gurucharan Srinivas; Anh Tuan Tran; Abhishek Vivekanandan; Ya Wang; Florian Wasserrab; Tino Werner; Christian Wirth; Stefan Zwicklbauer
arXiv, 2022.


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.


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