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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

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Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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相关实验视频

Updated: Jan 17, 2026

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
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一个基于视觉感知的终端到终端自动驾驶模型,用于临时道路.

Qinghua Su1, Min Xie1, Liyong Wang1

  • 1Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing, China.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

本研究介绍了一种端到端的深度学习模型,用于在临时道路上自动驾驶,提高移动机器人的安全性和稳定性. 该模型准确地识别临时道路,并实现无人地面车辆跟踪的实时性能.

关键词:
自动驾驶自动驾驶的自动驾驶.深度神经网络是一个神经网络.一个端到端的模型.美国UGVV

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 自动驾驶的深度学习在结构化道路上取得了进展,但对临时道路的研究有限.
  • 端到端模型提供高度集成,将输入数据直接转化为驾驶操作,提高安全性和稳定性.
  • 在临时道路等非结构化环境中的自主导航存在独特的挑战.

研究的目的:

  • 为移动机器人设计的专为临时道路设计的全自动驾驶模型.
  • 提高在充满挑战的非结构化环境中运行的自动驾驶汽车的安全性和稳定性.
  • 让无人驾驶地面车辆能够在临时道路上有效执行跟踪任务.

主要方法:

  • 开发了一个新的端到端模型,利用三个道路图像作为输入.
  • 使用全球上下文视觉转换器 (GCViT) 网络提取图像特征.
  • 使用变压器和门式反复单元 (GRU) 网络进行了局部路径规划,通过控制模型输出转向角度.

主要成果:

  • 端到端模型证明了临时道路的准确识别.
  • 单轨迹规划时间平均约为100 ms.
  • 平均轨迹偏差记录在0.689米,满足实时处理需求.

结论:

  • 拟议的端到端模型对在临时道路上自动驾驶有效.
  • 该模型的实时性能使低速无人驾驶车辆能够在临时的道路环境中执行跟踪任务.
  • 这项研究有助于在非结构化和动态道路条件下推进自主导航能力.