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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...
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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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Vertical curves provide the transition between two roadway grades, ensuring safety, comfort, and functionality. Calculating elevations at specific stations along the curve involves several systematic steps based on the curve's geometry and provided design parameters.The vertical curve is defined by its length, grades, Point of Vertical Intersection (P.V.I.) location, and P.V.I. elevation. The stations of the Point of Vertical Curvature (P.V.C.), where the curve begins, and the Point of Vertical...
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Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the...
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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Machine Vision-Based Method for Estimating Lateral Slope of Structured Roads.

Yunbing Yan1, Haiwei Li1

  • 1School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a novel machine vision method for estimating road lateral slope, crucial for vehicle stability. The approach accurately calculates slope using lane line analysis, validated through simulations.

Keywords:
SCNN algorithmimage-perspective principlemachine visionroad lateral-slope estimation

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Area of Science:

  • Computer Vision
  • Automotive Engineering
  • Robotics

Background:

  • Vehicle control and stability research often assumes known road lateral slope.
  • Limited studies focus on accurate road lateral-slope estimation methods.

Purpose of the Study:

  • To develop a machine vision-based method for structured-road lateral-slope estimation.
  • To provide reliable slope data for subsequent vehicle control and stability research.

Main Methods:

  • Utilizing machine vision to extract lane-line features.
  • Applying the Spatial CNN (SCNN) algorithm to fit lane-line functions.
  • Calculating road lateral slope based on image-perspective principles and lane line tangent slope.
  • Validating the method through off-line simulations using Prescan software.

Main Results:

  • The proposed method effectively estimates road lateral slope using lane line information.
  • Simulation results confirm the accuracy and reliability of the developed estimation technique.
  • The approach establishes a link between lane line tangent slope and road lateral slope.

Conclusions:

  • The machine vision-based method provides an accurate and effective solution for road lateral-slope estimation.
  • This technique enhances data availability for vehicle stability and control studies.
  • The study demonstrates the feasibility of using lane line analysis for real-time slope estimation.