  Developing tools in the context of autonomous systems [22, 24 ], such as
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
