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https://doi.org/10.21256/zhaw-30117| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | SensoDat : simulation-based sensor dataset of self-driving cars |
| Authors: | Birchler, Christian Rohrbach, Cyrill Kehrer, Timo Panichella, Sebastiano |
| et. al: | No |
| DOI: | 10.21256/zhaw-30117 |
| Conference details: | 21st International Conference on Mining Software Repositories (MSR), Lisbon, Portugal, 15-16 April 2024 |
| Issue Date: | 2024 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Language: | English |
| Subject (DDC): | 005: Computer programming, programs and data |
| Abstract: | Developing 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.10307479 |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30117 |
| Related research data: | https://doi.org/10.5281/zenodo.10307479 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Published as part of the ZHAW project: | COSMOS – DevOps for Complex Cyber-physical Systems of Systems |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_Birchler-etal_SensoDat-data-showcase-SDC.pdf | Accepted Version | 245.73 kB | Adobe PDF |  View/Open |
Show full item record
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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