Kuura: Leveraging Eclipse Kuksa in Vehicular Data Collection and Digital Twin Creation Environment
Timonen, Olli; Bomström, Toni; Stafford, Nicklas; Määttä, Samuli; Mahmoodi, Alireza Bakhshi Zadi; Päivärinta, Tero; Peltonen, Ella
CEUR-WS.org
Timonen, O., Bomström, T., Stafford, N., Määttä, S., Mahmoodi, A. B. Z., Päivärinta, T., & Peltonen, E. (2024). Kuura: leveraging Eclipse KUKSA in vehicular data collection and digital twin creation environment. In J. Kasurinen, T. Päivärinta, & T. Vartiainen (Eds.), Proceedings of the Annual Doctoral Symposium of Computer Science 2024. CEUR workshop proceedings, 3776, 131-142.
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© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202411016560
https://urn.fi/URN:NBN:fi:oulu-202411016560
Tiivistelmä
Abstract
Increased sensing and computing capabilities in cars are crucial for advanced traffic and driving automation. However, novel data delivery, testing, and machine learning pipelines are still needed to harness the full capabilities of automotive sensing solutions. At the same time, vehicular digital twins are needed to enable versatile testing and simulation capabilities. This paper depicts the Vehicle-In-The-Loop (VIL) cloud interface and verifies data consistency regardless of the source. The study aims to determine how data collected from simulation corresponds to real test drive data. The data is collected from both simulation and actual test drives. Utilising the MQTT protocol, data is stored on a cloud server and further fed into Unreal Engine 5, where the test drive is replayed, and its correspondence to the real drive is ensured. This work offers a new perspective on verifying data consistency between simulated and real test drives and complements the vehicle abstraction opportunities provided by Eclipse KUKSA. Our results highlight digital twin creation as a part of automotive software development and set premises for testing and validating complex use cases, such as traffic accidents and extreme weather, that can rarely or only with severe expenses be tested in real-life situations.
Increased sensing and computing capabilities in cars are crucial for advanced traffic and driving automation. However, novel data delivery, testing, and machine learning pipelines are still needed to harness the full capabilities of automotive sensing solutions. At the same time, vehicular digital twins are needed to enable versatile testing and simulation capabilities. This paper depicts the Vehicle-In-The-Loop (VIL) cloud interface and verifies data consistency regardless of the source. The study aims to determine how data collected from simulation corresponds to real test drive data. The data is collected from both simulation and actual test drives. Utilising the MQTT protocol, data is stored on a cloud server and further fed into Unreal Engine 5, where the test drive is replayed, and its correspondence to the real drive is ensured. This work offers a new perspective on verifying data consistency between simulated and real test drives and complements the vehicle abstraction opportunities provided by Eclipse KUKSA. Our results highlight digital twin creation as a part of automotive software development and set premises for testing and validating complex use cases, such as traffic accidents and extreme weather, that can rarely or only with severe expenses be tested in real-life situations.
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