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A Survey on Knowledge Graph-based Methods for Automated Driving
However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data.
Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems.
Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning
We show that our knowledge graph approach can reduce sign search space by 98. 9%.
Neuro-symbolic Architectures for Context Understanding
Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI).
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice
With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data.
Semantic, Cognitive, and Perceptual Computing: Advances toward Computing for Human Experience
Toward this goal, we discuss computing paradigms of semantic computing, cognitive computing, and an emerging aspect of computing, which we call perceptual computing.
