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自动驾驶车辆乘客舒适性标准:加速度和加加速度。

Standards for passenger comfort in automated vehicles: Acceleration and jerk.

机构信息

Delft University of Technology, Department of Cognitive Robotics, Mekelweg 2, Delft, 2628CD, the Netherlands.

出版信息

Appl Ergon. 2023 Jan;106:103881. doi: 10.1016/j.apergo.2022.103881. Epub 2022 Sep 2.

DOI:10.1016/j.apergo.2022.103881
PMID:36058166
Abstract

A prime concern for automated vehicles is motion comfort, as an uncomfortable ride may reduce acceptance of the technology amongst the general population. However, it is not clear how transient motions typical for travelling by car affect the experience of comfort. Here, we determine the relation between properties of vehicle motions (i.e., acceleration and jerk) and discomfort empirically, and we evaluate the ability of normative models to account for the data. 23 participants were placed in a moving-base driving simulator and presented sinusoidial and triangular motion pulses with various peak accelerations (A0.4 - 2 ms) and jerks (J0.5 - 15 ms), designed to recreate typical vehicle accelerations. Participants provided discomfort judgments on absolute 'Verbal Qualifiers' and relative 'Magnitude Estimates' associated with these motions. The data show that discomfort increases with acceleration amplitude, and that the strength of this effect depends on the direction of motion. We furthermore find that higher jerks (shorter duration pulses) are considered more comfortable, and that triangular pulses are more comfortable than sinusoidal pulses. ME responses decrease (i.e., reduced discomfort) with increasing pulse duration. Evaluations of normative models of vibration and shock (ISO 2631), and perceived motion intensity provide mixed results. The vibration model could not account for the data well. Reasonable agreement between predictions and observations were found for the shock model and perceived intensity model, which emphasize the role of acceleration. We present novel statistical models that describe motion comfort as a function of acceleration, jerk, and direction. The present findings are essential to develop motion planning algorithms aimed at maximizing comfort.

摘要

自动驾驶汽车的首要关注点是行驶舒适性,因为不舒适的驾乘体验可能会降低公众对这项技术的接受程度。然而,目前尚不清楚汽车行驶过程中的瞬时运动如何影响舒适感。在这里,我们通过实验确定了车辆运动(即加速度和加加速度)特性与不舒适感之间的关系,并评估了规范模型对数据的解释能力。23 名参与者被置于移动基础驾驶模拟器中,并呈现出具有不同峰值加速度(A0.4-2ms)和加加速度(J0.5-15ms)的正弦和三角运动脉冲,这些设计旨在重现典型的车辆加速度。参与者对这些运动的绝对“口头评价”和相对“量级估计”提供了不舒适感的判断。数据表明,不舒适感随加速度幅值的增加而增加,且这种影响的强度取决于运动的方向。我们还发现,较高的加加速度(较短的脉冲持续时间)被认为更舒适,并且三角脉冲比正弦脉冲更舒适。随着脉冲持续时间的增加,ME 响应减小(即不舒适感降低)。对振动和冲击的规范模型(ISO 2631)以及感知运动强度的评估结果喜忧参半。振动模型无法很好地解释数据。冲击模型和感知强度模型的预测与观察结果之间存在合理的一致性,这两个模型都强调了加速度的作用。我们提出了新的统计模型,将运动舒适性描述为加速度、加加速度和运动方向的函数。这些发现对于开发旨在最大程度提高舒适度的运动规划算法至关重要。

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