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An Application of the Canal-Otolith Interaction Model for Tilt-Coordination During a Braking Maneuver

机译:运河-耳石相互作用模型在制动过程中的倾斜协调中的应用

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In motion simulation, motion cueing algorithms are used to transform vehicle inertial cues into inertial cues that are within the simulator physical limits. Most motion cueing algorithms aim to minimize the error between these vehicle inertial cues and those generated by the simulator. Although this is one way to approach the problem, one can also aim to minimize the error between the perceived cues in the vehicle and the ones in the simulator. In this paper we designed a motion cueing algorithm based on a self-motion perception model. An experiment involving a braking maneuver was developed were subjects compared this perceptual motion cueing algorithm with a classical washout filter and a road rumble algorithm. Results showed no significant differences between the perceptual and the classical washout algorithms for both subjective and objective measures. However, we found significant differences in driving behavior between these two motion cueing algorithms and the road rumble algorithm. This study showed that inertial feedback has a positive influence in driving behavior. Therefore, this feedback might be essential to correctly simulate a braking maneuver.
机译:在运动仿真中,运动提示算法用于将车辆惯性提示转换为模拟器物理限制内的惯性提示。大多数运动提示算法旨在使这些车辆惯性提示与模拟器生成的惯性提示之间的误差最小。尽管这是解决问题的一种方法,但也可以旨在使车辆中的提示与模拟器中的提示之间的误差最小化。在本文中,我们设计了一种基于自我运动感知模型的运动提示算法。研究人员进行了一项涉及制动动作的实验,受试者将这种知觉运动提示算法与经典的冲刷滤波器和道路隆隆声算法进行了比较。结果表明,在主观和客观测量方面,知觉和经典冲刷算法之间没有显着差异。但是,我们发现这两种运动提示算法和道路隆隆声算法在驾驶行为上存在显着差异。这项研究表明,惯性反馈对驾驶行为具有积极影响。因此,此反馈对于正确模拟制动操作可能是必不可少的。

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