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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis.

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Current autonomous driving systems (ADS) depend on expensive, frequently updated high-definition (HD) maps.
  • This reliance limits the operational design domains (ODD) of ADS.
  • Online map creation offers a cost-effective alternative to traditional HD mapping.

Purpose of the Study:

  • To provide a state-of-the-art review of drivable area estimation techniques for ADS.
  • To introduce a novel architectural breakdown for analyzing both learning-based and non-learning-based algorithms.
  • To offer practical insights for deploying ADS in environments with limited or no HD map availability.

Main Methods:

  • A comprehensive review of recent and impactful drivable area estimation algorithms.
  • A proposed novel architecture for categorizing and analyzing various algorithmic approaches.
  • Analysis of the influence of modern sensing technologies on drivable area estimation.

Main Results:

  • The review categorizes and analyzes a range of drivable area estimation techniques.
  • A framework is presented to compare learning-based and non-learning-based methods.
  • Key datasets for evaluating and benchmarking drivable area estimation algorithms are identified.

Conclusions:

  • Drivable area estimation is essential for expanding ADS operational capabilities beyond HD map coverage.
  • The proposed architectural breakdown facilitates a deeper understanding of existing and future algorithms.
  • The review provides valuable resources for researchers and practitioners in autonomous driving.