Mechanisms of Visual Information Acquisition in Driving
Mechanisms of Visual Information Acquisition in Driving
批准号:
RGPIN-2021-02730
负责人:
Wolfe, Benjamin
金额:
$2.04万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31
中文摘要
想象一下,你正在街上开车,有人走到你前面,没有看你是否看到他们——你能多快意识到情况已经改变?你需要知道什么才能及时停车,你如何及时了解你需要知道的情况以避免撞车?你的车需要知道你所知道的哪些信息?知道所有这些信息如何使每个人的道路更安全?目前,人们正在推动汽车具备更多的自动驾驶功能,但要想安全地做到这一点,就意味着我们必须比我们自己更全面地了解坐在驾驶座上的人。例如,如果你的车正载着你去上班,但遇到了它无法处理的情况,要求你接手,你需要什么才能理解发生了什么,更不用说避开刚刚踩在路上的驼鹿了?从根本上说,这是一个人类视觉感知的问题,而不是一个工程问题:司机如何理解动态的、现实世界的情况,比如他们前面的道路?车手没有时间看世界上的每一件事,所以他们如何在需要的时候获得所需的信息,他们如何学会这样做,以及他们的能力如何随着年龄的增长而变化?我们可以看一张真实场景的照片,并在眨眼之间理解它,利用来自整个图像的信息,不仅仅是我们看的地方,而是当一切都在移动时,我们在真实情况下做什么?我们如何在道路上做到这一点,物体在我们周围移动,我们在世界中移动?对驾驶员行为的研究讨论了“情境意识”的概念,即我们对环境和可能发生的事情的意识,但没有问我们如何获得发展这种意识所需的信息。这个建议建立在我的信息获取工作的基础上,我在那里假设我们需要了解的世界的大部分信息都是通过周边视觉获得的,也就是说,从我们视野的其余部分获得的,远离我们现在所看的地方,只有当我们需要更多关于特定事物的细节时,我们才会关注世界上特定的事物或位置。在这个提案中,我的问题是:1)司机在开车时什么时候需要看特定的东西或位置,什么时候他们视野的其他部分能满足他们的需要?2)司机需要多少环境信息,他们不需要什么?3)司机是如何学会平衡我们对信息的需求和他们获取信息的工具的?随着我们的学习和年龄的增长,这种平衡又会发生什么变化?这些问题的答案对于设计下一代车辆至关重要,因为下一代车辆要考虑驾驶员能感知什么和不能感知什么,而发展这种理解将有助于我们更好地理解我们如何感知世界,并有助于使每个人的道路更安全。
英文摘要
Imagine you are driving down the street, and someone steps into the road ahead of you without looking to see if you have seen them - how quickly can you understand that the situation has changed? What do you need to know to stop in time, and how do you learn what you need to know about the situation in time to avoid a crash? What does your car need to know about what you know, and how can knowing all of this make the road safer for everyone? Right now, there is a push for more self-driving features in cars, but doing this safely means that we have to understand the person in the driver's seat more completely than we do. If, for example, your car is driving you to work, but encounters a situation it cannot handle and asks you to take over, what does it take for you to understand what is going on, much less to avoid the moose that just stepped into the road? Fundamentally, this is a question of human visual perception more than it is a question of engineering: how do drivers understand dynamic, real-world situations, like the road ahead of them? Drivers do not have time to look at everything in the world before events overtake them, so how do they get the information they need when they need it, how do they learn to do this, and how do their abilities change as they age? We can look at a photo of a real-world scene and understand it in the blink of an eye, using information from across the entire image, not just where we are looking, but what do we do in real situations when everything is moving? How do we do this on the road, where objects move around us and we move through the world? Research on driver behavior talks about the concept of Situation Awareness, our awareness of our environment and what might happen, but does not ask how we get the information we need to develop this awareness. This proposal builds on my work on Information Acquisition, where I hypothesize that much of what we need to know about the world we acquire with peripheral vision, that is, from the rest of our field of view away from where we are looking now, and that we look at specific things or locations in the world only when we need more detail about something specific. In this proposal, I ask: 1)When do drivers need to look at particular things or locations when driving, and when does the rest of their visual field give them what they need? 2)How much information about our environment do drivers need and what can they do without? 3)How do drivers learn to balance our need for information with the tools they have to acquire it, and how does this change as we learn and age? Answers to these questions are critical for designing next-generation vehicles that account for what drivers can and cannot perceive, and developing this understanding will help us better understand how we perceive the world, and help make the road safer for everyone.
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会议论文
Mechanisms of Visual Information Acquisition in Driving
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批准号:RGPIN-2021-02730
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.04万
-
财政年份:2021
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负责人:Wolfe, Benjamin
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依托单位:
Mechanisms of Visual Information Acquisition in Driving
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批准号:DGECR-2021-00150
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项目类别:Discovery Launch Supplement
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资助金额:$0.91万
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财政年份:2021
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负责人:Wolfe, Benjamin
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依托单位:
国内基金
海外基金
基于多幅图象的Visual Hull重构及表面属性建模算法研究
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批准号:60373031
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项目类别:面上项目
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资助金额:23.0万元
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批准年份:2003
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负责人:陈越
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依托单位: