From young to old: The effects of information presentation type, multimodal display, and age on situation awareness and processing time in automated vehicles

信息处理 情感(语言学) 障碍物 认知 介绍(产科) 计算机科学 心理学 应用心理学 人为因素与人体工程学 形势意识 人机交互 毒物控制 认知心理学 工程类 医学 沟通 医疗急救 航空航天工程 法学 神经科学 放射科 政治学
作者
Kimberly D. Martinez,Gaojian Huang
出处
期刊:Transportation Research Part F-traffic Psychology and Behaviour [Elsevier]
卷期号:103: 128-140 被引量:2
标识
DOI:10.1016/j.trf.2024.04.002
摘要

Research has revealed that conditionally automated vehicles can adversely affect situation awareness, a crucial factor in ensuring a safe transition of driving control during takeover, particularly for older adults. The objective of this study was to design and test more complex multimodal interfaces capable of delivering critical real-time road information, including obstacle locations, statuses, and lane availability, to effectively assist drivers who may experience age-related cognitive and physical declines, when navigating complex automated systems. This study investigated the effects of displays (single tactile, and visual and tactile combined), information presentation type (instructional, informative, baseline), and age (older and younger adults) on participants' takeover performance (i.e., information processing time and situation awareness). In general, the utilization of informative information resulted in an enhancement of drivers' situation awareness and information processing time, compared to when using the instructional information type. Moreover, multimodal displays were associated with faster processing speeds compared to unimodal displays. However, no significant main effect of the display was observed on the level of situation awareness. Likewise, there was no discernible age-related disparity in situation awareness levels. Yet, younger adults exhibited shorter information processing times than older adults. This research aims to improve situation awareness and processing time to help drivers, especially older adults, prevent time-critical accidents during the takeover process in automated driving. The findings from this study may inform the design of next-generation in-vehicle human–machine interfaces.
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