自动检测铁路传感器数据标注错误，提升自动驾驶训练数据质量
Automated Quality Check of Sensor Data Annotations
- 提出九种常见错误的自动检测方法，基于多传感器数据
- 六种方法精度达100%，其余两种分别达到96%和97%
- 开源工具可显著减少人工质检工作量，适合自动驾驶数据团队
路线与轨道环境监控在自动驾驶中至关重要。例如，在自动化等级GoA2下，可作为辅助系统协助司机监控路线；在完全自动化、无司机的GoA4级驾驶中，此类系统将独立完成环境监控。借助人工智能（AI），它们能自动响应路线上的风险与危险事件。为训练此类AI算法，需大量高质量训练数据，因其安全相关性要求极高。本文提出一种自动保证训练数据质量的方法，显著降低人工工作量，加速系统开发。我们设计了一个开源工具，用于检测铁路车辆多传感器数据集中的九类常见错误。通过人工验证评估框架性能，六种错误检测方法实现100%精确率，其余三种分别达到96%和97%的精确率。
原文摘要 · Abstract (English)
The monitoring of the route and track environment plays an important role in automated driving. For example, it can be used as an assistance system for route monitoring in automation level Grade of Automation (GoA) 2, where the train driver is still on board. In fully automated, driverless driving at automation level GoA4, these systems finally take over environment monitoring completely independently. With the help of artificial intelligence (AI), they react automatically to risks and dangerous events on the route. To train such AI algorithms, large amounts of training data are required, which must meet high-quality standards due to their safety relevance. In this publication we present an automatic method for assuring the quality of training data, significantly reducing the manual workload and accelerating the development of these systems. We propose an open-source tool designed to detect nine common errors found in multi-sensor datasets for railway vehicles. To evaluate the performance of the framework, all detected errors were manually validated. Six issue detection methods achieved 100% precision, while three additional methods reached precision rates 96% and 97%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。