Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity
Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity
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DOI：
10.1177/00187208221142126
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发表时间：
2023-01
期刊：
影响因子：
3.3
通讯作者：
A. Kamaraj;Joonbum Lee;Joshua E. Domeyer;Shu-Yuan Liu;John D. Lee
中科院分区：
文献类型：
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作者：
A. Kamaraj;Joonbum Lee;Joshua E. Domeyer;Shu-Yuan Liu;John D. Lee
目的本研究探讨主客观驾驶风格的相似性，以确定如何利用相似性来开发驾驶员兼容的车辆自动化。交互伙伴执行任务方式的相似性可以主观地通过问卷调查来衡量，也可以客观地通过描述每个代理的行为来衡量。虽然主观测量在预测方面有优势，但在实施基于这些测量的干预措施时，客观测量更有用。因此，展示客观和主观相似性是如何关联的，对于使未来的机器性能与人类偏好保持一致是谨慎的。方法采用走走停停场景下的驾驶模拟器研究。参与者经历了保守、温和和激进的自动驾驶风格，并对他们自己的驾驶风格和自动驾驶风格之间的相似性进行了评分。采用动态时间扭曲、欧几里得距离和时间对齐三种距离度量来计算手动和自动驾驶速度轮廓的客观相似度。采用线性混合效应模型考察了停止剖面的不同组成部分和三种客观相似测度对主观相似度的预测。结果欧几里得距离对主观相似度的预测效果最好。然而，这只观察到参与者接近十字路口的情况，而不是他们离开的情况。开发驾驶员认为与自己相似的驾驶风格是实现驾驶员兼容自动化的重要一步。在确定什么构成相似性时，重要的是：(a)使用反映驾驶员对相似性感知的措施，以及(b)了解驾驶风格的哪些因素控制主观相似性。
Objective This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. Background Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent’s actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. Methods A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. Results Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants’ approach to the intersection and not their departure. Conclusion Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver’s perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.