Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems

TL;DR

This paper introduces Knowledge-based Entity Prediction (KEP), a new approach to enhance machine perception in autonomous systems by leveraging relational knowledge to predict unrecognized entities, demonstrated in autonomous driving and manufacturing.

Contribution

The paper formally defines KEP as a knowledge completion task and proposes three machine learning solutions, applying them to real-world autonomous systems.

Findings

KEP improves perception accuracy in autonomous systems.

Three machine learning solutions for KEP are proposed.

Demonstrated effectiveness in autonomous driving and manufacturing.

Abstract

Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems. KEP leverages relational knowledge from heterogeneous sources in predicting potentially unrecognized entities. In this paper, we provide a formal definition of KEP as a knowledge completion task. Three potential solutions are then introduced, which employ several machine learning and data mining techniques. Finally, the applicability of KEP is demonstrated on two autonomous systems from different domains; namely, autonomous driving and smart manufacturing. We argue that in complex real-world systems, the use of KEP would significantly improve machine perception while pushing the current technology one step closer to achieving full autonomy.