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Title	An Overview Of Knowledge Representation Learning Based On Er Knowledge Graph
ID_Doc	8926
Authors	Singh B.
Year	2025
Published	Knowledge Graph-Based Methods for Automated Driving
DOI	http://dx.doi.org/10.1016/B978-0-443-30040-0.00002-3
Abstract	Autonomous driving is extremely promising with the potential to improve safety, lessen traffic, alter urban mobility, and lessen the environmental effect of transportation. The field of urban transportation is quickly changing due to autonomous driving, which offers safer, more effective, and environmentally friendly mobility options. Knowledge graphs and machine learning play a crucial role in the setting of smart cities, where connected infrastructure and data-driven technology are pervasive. Autonomous vehicles use knowledge graphs as their foundation to traverse intricate urban areas. These organized information representations incorporate data from a variety of sources, such as environmental conditions, real-time traffic updates, and road networks. The real-time decision-making by autonomous cars is made possible by this extensive information collection. This chapter explores how knowledge graphs and machine learning are essential for enabling safe autonomous driving in the intricate urban settings of smart cities. © 2025 Elsevier Inc. All rights reserved.
Author Keywords	Automation; Knowledge graphs; Machine learning; Smart cities; Traffic congestion

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