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年纪大了怎么开车?视觉协调是关键

 2018/11/9 8:49:19 《最新论文》 作者:European Transport Research Review 我有话说(0人评论) 字体大小:+

论文标题:A psycho-Geoinformatics approach for investigating older adults’ driving behaviours and underlying cognitive mechanisms

期刊:European Transport Research Review

作者:Qian (Chayn) Sun, Jianhong (Cecilia) Xia, Jonathan Foster, Torbjörn Falkmer and Hoe Lee

发表时间:2018/08/07

数字识别码:10.1186/s12544-018-0308-6

原文链接:https://etrr.springeropen.com/articles/10.1186/s12544-018-0308-6?utm_source=WeChat&utm_medium=Website_linksSocial_media_organic&utm_content=CelZha-MixedBrand-multijournal-Multidisciplinary-China&utm_campaign=ORG_AWA_CZH_BMCWechat_dailyposts_blogs

微信链接:https://mp.weixin.qq.com/s/TkPHwv5VTwdB_clvLUmMAA

原文作者:Qian (Chayn) Sun, Jianhong (Cecilia) Xia, Jonathan Foster, Torbjörn Falkmer and Hoe Lee

随着各国人口平均年龄的不断上升,越来越多的老年人开车可能会带来安全问题。最近在European Transport Research Review上发表的论文中,来自澳大利亚墨尔本皇家理工大学的Qian (Chayn) Sun 博士及其同事使用跨学科方法分析了开车这一行为,他们认为年龄并非是开车能力的唯一决定因素。

澳大利亚的人口老龄化使“老年人司机”这一问题更加突出。据估计,到2030年,澳大利亚约超有四分之一的驾车人员年龄超过65岁。另一方面,交通状况也变得日益复杂。但并不是所有的老年人开车都会发生安全问题,并且统计数据也不能代表个人的驾驶能力。虽然世界各国都很重视老年人的活动能力,但目前面临的最大挑战是如何制定合适的评估方法来识别哪些老年人更可能发生交通事故,并尽早提供干预措施。

Qian (Chayn) Sun 博士及其同事进行的这项跨学科研究使用心理-地理信息学方法对老年驾驶员的驾驶行为进行了分析,发现仅依靠年龄无法预测老年人开车时保持车道稳定性的能力。相反,他们的认知能力,特别是视觉注意力和执行功能,能很好地预测其开车的能力。

受试者正在参加道路驾车能力测试和实验室认知功能测试

更准确地了解老年人的驾驶行为

该研究对驾驶员-车辆-环境的交互作用进行了综合评估,来分析老年驾驶员的驾驶能力和行为,并探索了驾驶行为与认知功能之间的关联。研究人员通过眼睛跟踪、全球导航卫星系统(GNSS)跟踪和地理信息系统(GIS)等多种方法收集并分析了50名老年驾驶员的数据。结果表明,老年驾驶员的选择性注意力、空间判断力以及执行能力与能否保持直行、视觉搜索能力和协调功能具有显著相关关系。视觉-运动协调能力的测量能敏感且有效地评估老年人的开车行为。

研究人员还开发出了一些新的模型,比如视觉-运动协调模型,这个模型可以追踪驾驶员眼球随着车辆运动的变化而发生的变化和移动。其主要目的是了解汽车运动和驾驶员认知感知之间的相关性,包括感知后如何快速地做出驾驶变化。可以对视线的变化进行地理编码,同时可以使用GNSS来精确地记录汽车的运动,以了解眼球运动变化时汽车方向的每一个微小改变。事实上,在年龄较大的驾驶员中汽车运行方向和眼球运动之间的差异更加明显。研究表明,在交叉路口和在环形交叉口出口处右转时,老年驾驶员更难在感知后快速地做出驾驶变化。

© Chayn Sun

视觉-协调能力的发现

与许多其他研究相比,心理-地理信息学方法能够提供更多关于老年驾驶员的细节信息。视觉-运动协调能力评估是驾驶安全研究的新热点。关于认知能力对驾驶行为的影响,研究人员发现,对老年驾驶员来说视觉-空间能力和执行能力因素的影响最大。

驾驶的场景或任务能显著影响老年驾驶员的视觉模式,车道稳定性和协调能力。视觉-空间能力和视觉-运动协调能力欠佳与危险驾驶的行为似乎尤为密切。因此有足够的证据支持视觉-运动协调能力是评估老年人驾驶能力的有效而敏感的方法。老年人开车时必须充分认识其驾驶的能力和不足,这样才能采取防御性的车道保持措施以弥补不足,从而提高驾驶的安全性。

心理-地理信息学方法对于实现无人驾驶有何意义?

随着自动化驾驶的出现,认知注意成为了一个愈加重要的话题。当车辆能够实现自动化时,驾驶人员即使不再那么关注驾驶行为或周围交通状况也是相对安全的。但是一些自动化设计仍然需要人的监督控制,一旦发生状况,需要驾驶员及时恢复对汽车的控制。因此,老年人开车时仍要注意关注道路和周围的交通情况,以及保持有效的视觉-运动协调能力。

为了增强自动驾驶车辆的认知功能,使其更接近人工智能,我们需要一个基于某些先进认知理论的系统,例如文章中所描述的情境意识理论和视觉-运动协调理论。如何模拟驾驶驾驶环境中的人类行为仍是实现自动驾驶的一大难题。心理-地理信息学方法也可能因此长期应用于驾驶相关研究,比如,可以开发出一种心理-空间-时间算法并将其集成到无人驾驶汽车系统架构中。

点击阅读论文了解详情

摘要:

Introduction

Safe driving constantly challenges the driver’s ability to respond to the dynamic traffic scene under space and time constraints. It is of particular importance for older drivers to perform sufficient visual and motor actions with effective coordination due to the fact of age-related cognitive decline. However, few studies have been able to integrate drivers’ visual-motor behaviours with environmental information in a spatial-temporal context and link to the cognitive conditions of individual drivers. Little is known about the mechanisms that underpin the deterioration in visual-motor coordination of older drivers.

Development

Based on a review of driving-related cognitive decline in older adults and the context of driver-vehicle-environment interactions, this paper established a conceptual framework to identify the parameters of driver’s visual and motor behaviour, and reveal the cognitive process from visual search to vehicle control in driving. The framework led to a psycho-geoinformatics approach to measure older drivers’ driving behaviours and investigate the underlying cognitive mechanisms. The proposed data collection protocol and the analysis and assessments depicted the psycho-geoinformatics approach on obtaining quantified variables and the key means of analysis, as well as outcome measures.

Conclusions

Recordings of the driver and their interactions with the vehicle and environment at a detailed scale give a closer assessment of the driver’s behaviours. Using geoinformatics tools in driving behaviours assessment opens a new era of research with many possible analytical options, which do not have to rely on human observations. Instead, it receives clear indicators of the individual drivers’ interactions with the vehicle and the traffic environment. This approach should make it possible to identify lower-performing older drivers and problematic visual and motor behaviours, and the cognitive predictors of risky driving behaviours. A better targeted regulation and tailored intervention programs for older can be developed by further research.

阅读论文全文请访问:

https://etrr.springeropen.com/articles/10.1186/s12544-018-0308-6?utm_source=WeChat&utm_medium=Website_linksSocial_media_organic&utm_content=CelZha-MixedBrand-multijournal-Multidisciplinary-China&utm_campaign=ORG_AWA_CZH_BMCWechat_dailyposts_blogs

期刊介绍:

European Transport Research Review (ETRR) (https://etrr.springeropen.com/, 1.758 - 2-year Impact Factor)is a peer-reviewed open access journal publishing original high-quality scholarly research and developments in areas related to transportation science, technologies, policy and practice. Established in 2008 by the European Conference of Transport Research Institutes (ECTRI), the Journal provides researchers and practitioners around the world with an authoritative forum for the dissemination and critical discussion of new ideas and methodologies that originate in, or are of special interest to, the European transport research community. The journal is unique in its field, as it covers all modes of transport and addresses both the engineering and the social science perspective, offering a truly multidisciplinary platform for researchers, practitioners, engineers and policymakers.

来源:European Transport Research Review