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Approximating Road Geometry with Multisine Signals for Driver Identification

机译：利用多正弦信号逼近道路几何形状以识别驾驶员

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摘要

The understanding of human responses to visual information in car driving tasks requires the use of system identification tools that put constraints on the design of data collection experiments. Most importantly, multisine perturbation signals are required, including a multisine road geometry, to separately identify the different driver steering responses in the frequency domain. It is as of yet unclear, however, to what extent drivers steer differently along such multisine roads than they do for real roads. This paper presents a method for approximating real-world road geometries with multisine signals, and applies it to a stretch of road used in an earlier investigation into driver steering. In addition, a human-in-the-loop experiment is performed to collect driver steering data for both the realistic real-world road and its multisine approximation. Overall, the analysis of driver performance metrics and driver identification data shows that drivers adopt equivalent control behaviour when steering along both roads. Hence, the use of such multisine approximations allows for the realization of realistic roads and driver behaviour in car driving experiments, in addition to supporting the application of quantitative driver identification techniques for data analysis.
机译：要了解人类对汽车驾驶任务中视觉信息的反应，需要使用系统识别工具，这会限制数据收集实验的设计。最重要的是，需要多正弦扰动信号，包括多正弦道路几何形状，以在频域中分别识别不同的驾驶员转向响应。但是，目前尚不清楚，驾驶员在这种多正弦道路上的转向程度与实际道路上的转向程度不同。本文提出了一种使用多正弦信号逼近现实世界道路几何形状的方法，并将其应用于在驾驶员转向早期研究中使用的一段道路。此外，还进行了一个在环实验，以收集现实世界中的道路及其多正弦近似值的驾驶员转向数据。总体而言，对驾驶员性能指标和驾驶员身份数据的分析表明，驾驶员在两条道路上行驶时都采用同等的控制行为。因此，除了支持将定量驾驶员识别技术应用于数据分析之外，使用这种多正弦近似值还可以在汽车驾驶实验中实现逼真的道路和驾驶员行为。

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