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dc.contributor.authorBirchler, Christian-
dc.contributor.authorRohrbach, Cyrill-
dc.contributor.authorKehrer, Timo-
dc.contributor.authorPanichella, Sebastiano-
dc.date.accessioned2024-03-04T17:01:24Z-
dc.date.available2024-03-04T17:01:24Z-
dc.date.issued2024-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30117-
dc.description.abstractDeveloping tools and researching in the context of self-driving cars (SDCs) is time-consuming and costly since researchers and practitioners rely on expensive computing hardware and simulation software. We propose SensoDat, a dataset of 32,580 executed simulation-based SDC test cases generated with state-of-the-art test generators for SDCs. The dataset consists of trajectory logs and a variety of sensor data from the SDCs (e.g., rpm, wheel speed, brake thermals, transmission, etc.) represented as a time series. In total, SensoDat provides data from 81 different simulated sensors. Future research in the domain of SDCs does not necessarily depend on executing expensive test cases when using SensoDat. Furthermore, with the high amount and variety of sensor data, we think SensoDat can contribute to research, particularly for AI development, regression testing techniques for simulation-based SDC testing, flakiness in simulation, etc. Link to the dataset: https://doi.org/10.5281/zenodo.10307479de_CH
dc.language.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleSensoDat : simulation-based sensor dataset of self-driving carsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-30117-
zhaw.conference.details21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedSoftware Engineeringde_CH
zhaw.funding.zhawCOSMOS – DevOps for Complex Cyber-physical Systems of Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.relation.referenceshttps://doi.org/10.5281/zenodo.10307479de_CH
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Birchler, C., Rohrbach, C., Kehrer, T., & Panichella, S. (2024). SensoDat : simulation-based sensor dataset of self-driving cars. 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024. https://doi.org/10.21256/zhaw-30117
Birchler, C. et al. (2024) ‘SensoDat : simulation-based sensor dataset of self-driving cars’, in 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30117.
C. Birchler, C. Rohrbach, T. Kehrer, and S. Panichella, “SensoDat : simulation-based sensor dataset of self-driving cars,” in 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024, 2024. doi: 10.21256/zhaw-30117.
BIRCHLER, Christian, Cyrill ROHRBACH, Timo KEHRER und Sebastiano PANICHELLA, 2024. SensoDat : simulation-based sensor dataset of self-driving cars. In: 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 2024
Birchler, Christian, Cyrill Rohrbach, Timo Kehrer, and Sebastiano Panichella. 2024. “SensoDat : Simulation-Based Sensor Dataset of Self-Driving Cars.” Conference paper. In 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30117.
Birchler, Christian, et al. “SensoDat : Simulation-Based Sensor Dataset of Self-Driving Cars.” 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30117.


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