交通載具振動對於人體感知判斷分析

Abstract

本研究探討利用六軸模擬平台再現裝甲車乘坐時的動態環境，以進行乘員動態感知與辨識能力之研究。研究動機源自於裝甲車行駛時產生的劇烈振動與顛簸可能影響乘員的舒適度及任務執行，而在實車上進行實驗成本高且存在安全風險。因此，本研究結合六軸平台模擬裝甲車行駛中的振動，提供安全可控的實驗環境。該六軸平台具有三個方向的移動，以及三個方向的旋轉等六個自由度的運動方向，能產生接近真實車輛的加速度和姿態變化。在感知實驗中，受試者乘坐於模擬平台上體驗預定的裝甲車行駛情境，共有五種不同的道路條件。實驗設計邀請具有裝甲車乘坐經驗及無此經驗的兩組受試者參與，以比較先前乘坐經驗對辨識道路條件表現的影響。受試者需透過主觀辨識評分的方式，判斷所感知到的振動屬於何種情境。主觀評分採用問卷調查形式，記錄受試者對各模擬情境的辨識結果。實驗結果顯示，六軸平台所重現的裝甲車行駛振動具有良好的真實性，大多數受試者能夠根據平台提供的振動正確辨識出不同的行駛情境；進一步分析指出，受測者對特定方向的振動物理特徵具有較高敏感度，為後續模擬參數最佳化與感知模型建立提供參考依據。此外，具有實際乘坐經驗者與無經驗者在辨識表現上並無顯著差異，顯示本研究所建構之模擬系統具有良好的通用性，即便未具乘坐經驗的受測者亦能準確辨識模擬平台所呈現的動態情境，說明此模擬系統具有潛在訓練應用價值，未來可作為模擬作業與訓練環境建置之有效工具。

This study investigates the use of a six-degree-of-freedom (6-DOF) motion platform to replicate the dynamic environment experienced by armored vehicle occupants, with the goal of exploring human perception and road condition recognition abilities. The motivation for this work arises from the intense vibrations and jolts generated during armored vehicle operations, which may adversely affect crew comfort and mission performance. However, conducting experiments using real vehicles is both costly and potentially hazardous. To address this, the study employs a 6-DOF motion platform to simulate the vibrations encountered during armored vehicle travel, thereby providing a safe and controllable experimental environment. The platform enables translational motion along three axes and rotational motion around three axes, capable of generating accelerations and posture changes that closely resemble real-world conditions. In the perception experiments, participants experienced five distinct simulated road conditions while seated on the motion platform. Two groups—individuals with and without prior experience riding in armored vehicles—were recruited to evaluate the impact of prior experience on recognition performance.Participants were instructed to subjectively identify the perceived vibration scenario through questionnaire-based assessments. The results indicate that the vibrations generated by the platform were highly realistic, as most participants were able to accurately identify the simulated driving conditions based solely on vibration cues. Further analysis revealed that participants were particularly sensitive to vibration features along specific axes, suggesting the potential for optimizing stimulation patterns and developing perceptual models. Moreover, no significant difference in recognition performance was observed between experienced and inexperienced participants, demonstrating the generalizability and accessibility of the system. Even those without prior riding experience were able to accurately recognize the dynamic scenarios presented by the platform, highlighting its potential as an effective tool for simulation-based training and mission preparation.