Suppr超能文献

计划性手动驾驶对困意和接管请求响应的影响:对自动驾驶中驾驶员的理解的模拟研究。

Effects of scheduled manual driving on drowsiness and response to take over request: A simulator study towards understanding drivers in automated driving.

机构信息

Automotive Human Factors Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Japan.

Automotive Human Factors Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Japan.

出版信息

Accid Anal Prev. 2019 Mar;124:202-209. doi: 10.1016/j.aap.2019.01.013. Epub 2019 Jan 18.

Abstract

Because current automated vehicles have operational limitations, it is important to ensure that the fallback-ready driver is able to perform appropriately when required to take over control of the vehicle. However, time-related increase in driver drowsiness is well-known, and drowsy driving can affect response to take-over request (TOR). It was previously reported that a scheduled period of manual driving during automated driving was beneficial in maintaining driver arousal level. The present driving simulator study investigates the effects of scheduled manual driving on driver drowsiness and performance, as well as age differences therein. A total of 115 participants, whose gender was balanced and age was distributed uniformly from 20 to 70 years, drove an automated vehicle for 31 min, and a TOR was prompted before a collision event. A between-subjects design comprised two conditions: with versus without a scheduled 10-min interval of manual driving that ended 10 min before TOR. The Karolinska Sleepiness Scale and eyeblink durations estimated from electrooculograms (EOG) were used to subjectively and objectively measure participant's drowsiness. Reaction time, standard deviation of steering wheel angle, and minimum Time-to-Collison (TTC) were extracted to measure driver performance in response to TOR. The alleviating effect on drowsiness of 10-min scheduled manual driving became non-significant after another 10-min period of automated driving. Although the scheduled manual driving had no significant effect for younger drivers, older drivers reacted significantly more slowly in both steering and braking at the critical event. These findings provide essential insights for human-vehicle interactions: Scheduled manual driving cannot maintain drivers' arousal level for 10 min afterwards, and for older drivers, it would be better to avoid unnecessary task-switching between manual and automated driving.

摘要

由于当前的自动驾驶车辆存在操作限制,因此确保随时准备接管车辆的驾驶员在需要时能够适当地执行操作非常重要。然而,众所周知,驾驶员的困倦会随着时间的推移而增加,并且困倦驾驶会影响接管请求(TOR)的反应。先前有报道称,在自动驾驶期间安排一段时间的手动驾驶有助于维持驾驶员的警觉水平。本驾驶模拟器研究调查了安排手动驾驶对驾驶员困倦和表现的影响,以及其中的年龄差异。共有 115 名参与者,他们的性别均衡,年龄从 20 岁到 70 岁均匀分布,驾驶自动驾驶车辆 31 分钟,并在碰撞事件之前提示 TOR。采用了一种被试间设计,包括两种条件:有和没有安排的 10 分钟手动驾驶间隔,手动驾驶间隔在 TOR 前 10 分钟结束。使用 Karolinska 睡眠量表和眼电图(EOG)估算的眨眼持续时间来主观和客观地测量参与者的困倦程度。提取反应时间、方向盘角度标准差和最小碰撞时间(TTC)来衡量驾驶员对 TOR 的反应性能。在进行了另外 10 分钟的自动驾驶后,10 分钟计划手动驾驶对困倦的缓解作用变得不再显著。虽然对于年轻驾驶员来说,计划的手动驾驶没有显著影响,但在关键事件中,年长驾驶员在转向和制动方面的反应明显较慢。这些发现为人机交互提供了重要的见解:计划的手动驾驶不能在 10 分钟后维持驾驶员的警觉水平,对于年长驾驶员来说,最好避免在手动和自动驾驶之间不必要的任务切换。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验