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相关概念视频

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

49
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...
49
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

69
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
69
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

40
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
40
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

41
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
41
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

48
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...
48
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

75
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
75

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Updated: Jul 9, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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通过消除道路上的障碍物来检测道路上的障碍物.

Krzysztof Lis, Sina Honari, Pascal Fua

    IEEE transactions on pattern analysis and machine intelligence
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    此摘要是机器生成的。

    这项研究引入了一种新的方法来检测道路障碍物,通过涂上图像补丁来移除它们. 该系统通过检测原始图像和绘制图像之间的差异来识别障碍物,提高道路安全.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 自主驾驶系统 自主驾驶系统

    背景情况:

    • 道路障碍物检测对于自动驾驶汽车至关重要.
    • 预先记录所有可能阻碍培训的障碍是不可行的.
    • 现有的方法与新的或未经目录的障碍作斗争.

    研究的目的:

    • 为车辆开发一个强大的障碍物检测方法.
    • 为了应对之前未遇到的道路障碍物检测的挑战.
    • 提高自动驾驶系统的安全性和可靠性.

    主要方法:

    • 图像补丁是从道路场景中选择的.
    • 补丁中的障碍物被使用周围道路纹理的涂料去除.
    • 一个神经网络被训练来识别原始和染色补丁之间的差异.
    • 检测到的差异表明存在障碍物.

    主要成果:

    • 油漆技术有效地从选定的图像补丁中去除障碍物.
    • 差异检测网络准确地识别了移除障碍物的位置.
    • 该方法在检测广泛的以前未被记录的障碍物方面表现有前途.

    结论:

    • 这种新的方法提供了一种可行的解决方案,用于检测未经目录的道路障碍物.
    • 基于差异的检测方法提高了障碍物检测系统的稳定性.
    • 进一步开发可以显著提高自动驾驶汽车的安全性.