An Evolvable Knowledge Graph Supporting a Hybrid Intelligence Autonomous Driving System
Teern, Anna; Elgendy, Nada; Seppänen, Pertti; Päivärinta, Tero (2025-05-30)
IEEE
30.05.2025
A. Teern, N. Elgendy, P. Seppänen and T. Päivärinta, "An Evolvable Knowledge Graph Supporting a Hybrid Intelligence Autonomous Driving System," 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C), Odense, Denmark, 2025, pp. 557-564, doi: 10.1109/ICSA-C65153.2025.00083.
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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202506094244
https://urn.fi/URN:NBN:fi:oulu-202506094244
Tiivistelmä
Abstract
Route planning is influenced by various factors, with speed and distance often prioritized in map services. However, dynamic conditions such as road maintenance, weather, or accidents can significantly impact these routes. By leveraging hybrid intelligence (HI), the collaboration between human intuition and machine efficiency, we focus on supporting decision-making in autonomous and semi-autonomous driving contexts. This study explores integrating knowledge of the dynamic conditions into HI Autonomous Driving Systems (HI-ADS) within the 6G Visible Project. The findings demonstrate the potential of knowledge graphs (KGs) to enhance decision-making by integrating evolving data and ensuring adaptability to real-world driving conditions. Based on the design objectives of learning for evolvable KGs, concrete requirements for the HI-ADS KG are established.
Route planning is influenced by various factors, with speed and distance often prioritized in map services. However, dynamic conditions such as road maintenance, weather, or accidents can significantly impact these routes. By leveraging hybrid intelligence (HI), the collaboration between human intuition and machine efficiency, we focus on supporting decision-making in autonomous and semi-autonomous driving contexts. This study explores integrating knowledge of the dynamic conditions into HI Autonomous Driving Systems (HI-ADS) within the 6G Visible Project. The findings demonstrate the potential of knowledge graphs (KGs) to enhance decision-making by integrating evolving data and ensuring adaptability to real-world driving conditions. Based on the design objectives of learning for evolvable KGs, concrete requirements for the HI-ADS KG are established.
Kokoelmat
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