S2R-Bench:自动驾驶的SIM-to-Real评估基准
Li Wang1,2, Guangqi Yang3, Lei Yang4
1School of Machanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Scientific data
|December 4, 2025
概括
本研究介绍了自动驾驶感知系统的Sim-to-Real评估基准 (S2R-Bench). 它通过使用现实世界的数据来改善自动驾驶安全的稳定性来弥补当前基准的差距.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 感知算法对于自动驾驶安全至关重要,但缺乏稳定性.
- 现有的基准无法复制现实世界条件,如极端天气和传感器异常.
- 评估感知算法的可靠性是一个新兴的挑战.
研究的目的:
- 为自动驾驶提出一个新的Sim-to-Real评估基准 (S2R-Bench).
- 为了解决纯粹模拟基准的局限性.
- 促进对自动驾驶汽车更强大的感知模型的研究.
主要方法:
- 在各种现实世界的道路和天气条件中收集了各种传感器异常数据.
- 开发了一个基准数据集,涵盖了不同的照明强度和时间段.
- 将现实数据与模拟数据进行比较,以验证可靠性.
主要成果:
- S2R-Bench是第一个基于现实场景的腐败强度数据集.
- 证明了收集的现实世界数据的可靠性,用于评估.
- 突出了模拟和现实世界表现之间的差异.
结论:
- S2R-Bench为自动驾驶感知系统提供了一个现实的评估.
- 该数据集有助于开发更强大的感知算法.
- 这项工作对于推进自动驾驶技术的安全性和可靠性至关重要.
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