构建公开数据集，量化列车司机障碍识别反应性能。
Measuring Train Driver Performance as Key to Approval of Driverless Trains
- 基于711组受控实验，测量司机反应时间与距离
- 涵盖不同车速、障碍物尺寸等12种条件组合
- 为无人驾驶列车安全认证提供可量化的基准
欧盟实施条例（EU）No. 402/2013附件一中第2.1.4(b)、2.4.2(b)和2.4.3(b)条允许在功能与界面相似的前提下，简化对无人驾驶列车计算机视觉系统的安全审批。然而，人类司机最关键的障碍检测能力难以量化，因缺乏公开的测量数据。本文总结了现有研究成果，并通过711组受控实验构建了一个公开且匿名的数据集，测量了不同车速、障碍物尺寸、列车保护系统及颜色对比度下的反应时间和距障碍物距离。该数据集旨在为研究、标准化与监管提供客观、全面的基准支持，已发布于https://data.fid-move.de/de/dataset/atosensedata。
原文摘要 · Abstract (English)
Points 2.1.4(b), 2.4.2(b) and 2.4.3(b) in Annex I of Implementing Regulation (EU) No. 402/2013 allow a simplified approach for the safety approval of computer vision systems for driverless trains, if they have 'similar' functions and interfaces as the replaced human driver. The human driver is not replaced one-to-one by a technical system - only a limited set of cognitive functions are replaced. However, performance in the most challenging function, obstacle detection, is difficult to quantify due to the deficiency of published measurement results. This article summarizes the data published so far. This article also goes a long way to remedy this situation by providing a new public and anonymized dataset of 711 train driver performance measurements from controlled experiments. The measurements are made for different speeds, obstacle sizes, train protection systems and obstacle color contrasts respectively. The measured values are reaction time and distance to the obstacle. The goal of this paper is an unbiased and exhaustive description of the presented dataset for research, standardization and regulation. The dataset with supplementing information and literature is published on https://data.fid-move.de/de/dataset/atosensedata
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