Poster: Multimodal Data Analytics and Machine Learning for Software-Defined Vehicles
Kämä, Benjamin; Peltonen, Ella (2025-01-01)
IEEE
01.01.2025
B. Kämä and E. Peltonen, "Poster: Multimodal Data Analytics and Machine Learning for Software-Defined Vehicles," 2024 IEEE/ACM Symposium on Edge Computing (SEC), Rome, Italy, 2024, pp. 545-547, doi: 10.1109/SEC62691.2024.00074.
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© 2024 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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© 2024 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-202501161213
https://urn.fi/URN:NBN:fi:oulu-202501161213
Tiivistelmä
Abstract
The paradigm shift towards software-defined vehicles has been foreseen to give access to in-vehicular and external sensors, support intelligent applications and edge-based ML/AI methods, and provide new possibilities for intelligent traffic applications. The key challenges, however, include meaningfully combining, learning, and personalising this multimodal and multidimensional information. This poster highlights our ongoing work to define the vehicular data analysis landscape.
The paradigm shift towards software-defined vehicles has been foreseen to give access to in-vehicular and external sensors, support intelligent applications and edge-based ML/AI methods, and provide new possibilities for intelligent traffic applications. The key challenges, however, include meaningfully combining, learning, and personalising this multimodal and multidimensional information. This poster highlights our ongoing work to define the vehicular data analysis landscape.
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