Towards Data-Centric and Context-Aware Decision Making in Software-Defined Vehicles
Peltonen, Ella; Basnayake, Vishaka; Elgendy, Nada; Kämä, Benjamin; Seppänen, Pertti; Päivärinta, Tero (2025-05-30)
IEEE
30.05.2025
E. Peltonen, V. Basnayake, N. Elgendy, B. Kämä, P. Seppänen and T. Päivärinta, "Towards Data-Centric and Context-Aware Decision Making in Software-Defined Vehicles," 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C), Odense, Denmark, 2025, pp. 574-577, doi: 10.1109/ICSA-C65153.2025.00085
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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-202506064203
https://urn.fi/URN:NBN:fi:oulu-202506064203
Tiivistelmä
Abstract
As vehicular computing environments move towards software-defined paradigms and microservice architectures, more data becomes available from the in-vehicular sensors for various applications to be developed. Combining such local vehicular information with feedback from drivers and passengers, the environment, and external data sources opens new avenues for application development and enchanting vehicular capabilities, services, and systems. However, in managing more data effectively, reliably, securely, and privately, more focus is given to data analysis, machine learning, and artificial intelligence tasks and how they are integrated into vehicle-cloud architectures. This paper considers a vision for a data-centric architecture that utilizes open-source building blocks. We aim to bring together data from humans, vehicles, and environments to support novel application development and context-aware decision-making.
As vehicular computing environments move towards software-defined paradigms and microservice architectures, more data becomes available from the in-vehicular sensors for various applications to be developed. Combining such local vehicular information with feedback from drivers and passengers, the environment, and external data sources opens new avenues for application development and enchanting vehicular capabilities, services, and systems. However, in managing more data effectively, reliably, securely, and privately, more focus is given to data analysis, machine learning, and artificial intelligence tasks and how they are integrated into vehicle-cloud architectures. This paper considers a vision for a data-centric architecture that utilizes open-source building blocks. We aim to bring together data from humans, vehicles, and environments to support novel application development and context-aware decision-making.
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