Reliability and Models of Subjective Motion Incongruence Ratings in Urban Driving Simulations

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Human-Machine Systems Pub Date : 2024-09-18 DOI:10.1109/THMS.2024.3450831
Maurice Kolff;Joost Venrooij;Markus Schwienbacher;Daan M. Pool;Max Mulder
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Abstract

In moving-base driving simulators, the sensation of the inertial car motion provided by the motion system is controlled by the motion cueing algorithm (MCA). Due to the difficulty of reproducing the inertial motion in urban simulations, accurate prediction tools for subjective evaluation of the simulator's inertial motion are required. In this article, an open-loop driving experiment in an urban scenario is discussed, in which 60 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach's alpha and McDonald's omega. Results show that the better the motion cueing is rated, the lower the reliability of that rating data is, and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion, while congruent motion is less important. Furthermore, through a forward regression approach, it is shown that participants' rating behavior can be described by a first-order low-pass filtered response to the lateral specific force mismatch (66.0%), as well as a similar response to the longitudinal specific force mismatch (34.0%). By this better understanding of the acquired ratings in urban driving simulations, including their reliability and predictability, incongruences can be more accurately targeted and reduced.
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城市驾驶模拟中主观运动不协调评级的可靠性和模型
在移动基地驾驶模拟器中,运动系统提供的汽车惯性运动感觉由运动提示算法(MCA)控制。由于在城市模拟器中很难再现惯性运动,因此需要精确的预测工具来对模拟器的惯性运动进行主观评估。本文讨论了一个城市场景中的开环驾驶实验,其中 60 名参与者通过总体评分和连续评分方法对运动提示进行了评估。测试了代表不同运动提示质量水平的三种 MCA。通过对 Cronbach's alpha 和 McDonald's omega 的估计,研究了连续评分法在城市场景中提供可靠数据的条件。结果表明,对运动提示的评分越高,评分数据的可靠性就越低,连续评分和总体评分的相关性就越小。这表明,对运动质量的主观评价主要受不协调运动的(瞬间)影响,而协调运动则不那么重要。此外,通过前向回归方法,研究表明参与者的评分行为可以通过对横向特定力失配(66.0%)的一阶低通滤波响应以及对纵向特定力失配(34.0%)的类似响应来描述。通过更好地了解城市驾驶模拟中获得的评级,包括其可靠性和可预测性,可以更准确地锁定并减少不一致现象。
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来源期刊
IEEE Transactions on Human-Machine Systems
IEEE Transactions on Human-Machine Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
7.10
自引率
11.10%
发文量
136
期刊介绍: The scope of the IEEE Transactions on Human-Machine Systems includes the fields of human machine systems. It covers human systems and human organizational interactions including cognitive ergonomics, system test and evaluation, and human information processing concerns in systems and organizations.
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Table of Contents Call for Papers: IEEE Transactions on Human-Machine Systems IEEE Transactions on Human-Machine Systems Information for Authors IEEE Systems, Man, and Cybernetics Society Information IEEE Systems, Man, and Cybernetics Society Information
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