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Selected Data About Geographic Locations01:25

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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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...
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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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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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Optimal object association in theDempster-Shafer framework.

IEEE transactions on cybernetics·2014
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential

Chunlei Yu1,2, Veronique Cherfaoui2, Philippe Bonnifait2

  • 1State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, 10084 Beijing, China.

Sensors (Basel, Switzerland)
|January 16, 2020
PubMed
Summary

This study introduces a novel grid-based method for autonomous vehicles to semantically understand road spaces. It integrates prior map data and sensor readings using Dempster-Shafer theory to improve navigation safety and efficiency.

Keywords:
evidential occupancy gridlane gridprior mapsemanticuncertainty

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision and Machine Learning

Background:

  • Occupancy grids are standard for mobile robot navigation, encoding obstacles.
  • Autonomous vehicles require semantic scene understanding for safe road navigation.
  • Existing models struggle with localization uncertainty and integrating diverse data sources.

Purpose of the Study:

  • To develop a grid-based evidential approach for semantic road space modeling.
  • To enhance autonomous vehicle navigation by incorporating prior map and sensor data.
  • To address localization uncertainty in real-time road environments.

Main Methods:

  • Utilizing a grid-based evidential framework based on Dempster-Shafer theory.
  • Encoding road rules and lane-level information from prior maps into the grid.
  • Integrating real-time obstacle data from exteroceptive sensors.
  • Managing localization uncertainty through evidential reasoning.

Main Results:

  • Successfully modeled semantic road space with enhanced understanding of drivable areas.
  • Demonstrated effective integration of prior map data and sensor readings.
  • Showcased robust performance in handling localization uncertainty.
  • Validated through real-world road data with qualitative and quantitative analysis.

Conclusions:

  • The proposed evidential approach enhances semantic understanding of road environments for autonomous navigation.
  • The method effectively handles localization uncertainty and integrates multi-source information.
  • This approach offers a promising solution for real-time semantic mapping in autonomous driving.