SensoDat：基于仿真的自动驾驶汽车传感器数据集
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
AI总结：
本文提出SensoDat数据集，包含32,580个基于仿真的SDC测试用例及81类传感器时间序列数据，以降低自动驾驶测试成本并支持AI与回归测试研究。
摘要：
在自动驾驶汽车（SDC）等自主系统背景下开发工具[22,24]既耗时又昂贵，因为研究人员和从业者依赖昂贵的计算硬件与仿真软件。我们提出 SensoDat，这是一个包含32,580个已执行的、基于仿真的SDC测试用例的数据集，这些测试用例由最先进的SDC测试生成器生成。该数据集由轨迹日志和来自SDC的多种传感器数据（例如转速rpm、轮速、刹车温度、传动系统等）组成，并以时间序列表示。SensoDat总共提供来自81个不同仿真传感器的数据。使用SensoDat后，SDC领域的未来研究不必依赖执行昂贵的测试用例。此外，凭借大量且多样的传感器数据，我们认为SensoDat能够为研究作出贡献，特别是AI开发、基于仿真的SDC测试的回归测试技术、仿真中的不稳定性等方面。数据集链接：https://doi.org/10.5281/zenodo.10307479
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
