Marvin H. Cheng, Jinhua Guan, Hemal K. Dave, Robert S. White, Richard Whisler, Joyce V. Zwiener, Hugo E. Camargo, Richard S. Current
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引用次数: 0
摘要
各种职业的任务路线对驾驶员的职业安全起着至关重要的作用,事故原因因具体任务要求而异。本研究的重点是开发一套系统,通过优化驾驶员-车辆界面(DVI)来解决执法人员驾驶分心问题。执法车辆中的驾驶员-车辆界面(DVI)设计不佳,通常安装有售后市场的警用设备,可能导致感知-运动问题,如视线受阻、难以触及控制装置和操作失误,从而造成驾驶员分心。为了缓解这些问题,我们专门为执法车辆开发了一个驾驶模拟平台。开发过程涉及传感器的选择和安置,以监控驾驶员的行为以及与设备的交互。传感器选择的关键标准包括准确性、可靠性以及与现有车辆系统无缝集成的能力。根据先前的人体工程学研究和数字人体建模,对传感器位置进行了战略定位,以确保在不妨碍驾驶员视野或控制的情况下进行全面监控。我们的系统将传感器安装在仪表盘、方向盘和关键控制界面上,提供驾驶员与车辆设备交互的实时数据。我们设计了一个基于监督机器学习的预测模型来评估驾驶员的分心程度。应进一步研究传感器的配置位置和集成,以确保更新后的 DVI 减少驾驶员分心,支持更安全的任务驾驶操作。
Designing an Experimental Platform to Assess Ergonomic Factors and Distraction Index in Law Enforcement Vehicles during Mission-Based Routes
Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations.