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Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways.

Eun Seok Jang1, Jae Kyu Suhr2, Ho Gi Jung3

  • 1Department of Electronic Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju-si, Chungbuk 27469, Korea. esjang9919@gmail.com.

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Summary
This summary is machine-generated.

This study introduces lane endpoints as effective landmarks for vehicle localization in autonomous driving and advanced driver assistance systems (ADAS). The research details efficient detection and accuracy evaluation methods for these landmarks using monocular cameras.

Keywords:
intelligent vehiclelane endpoint detectionposition accuracy evaluationsensor fusionvehicle localization

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Area of Science:

  • Computer Vision
  • Robotics
  • Automotive Engineering

Background:

  • Landmark-based vehicle localization is crucial for autonomous driving and ADAS.
  • Existing landmarks like lane markings lack longitudinal position data.
  • Lane endpoints offer a potential solution for improved localization.

Purpose of the Study:

  • To propose and evaluate methods for detecting lane endpoints using monocular cameras.
  • To develop a reliable system for assessing the positional accuracy of detected lane endpoints.
  • To enhance vehicle localization capabilities for ADAS and autonomous driving.

Main Methods:

  • Lane endpoint detection involves lane detection, candidate generation, and verification.
  • Accuracy evaluation uses a mobile mapping system (MMS) for ground truth comparison.
  • Methods were tested on a dataset collected from 80 km of highway driving in various conditions.

Main Results:

  • The proposed lane endpoint detection method is computationally efficient.
  • The accuracy evaluation method reliably measures positional accuracy.
  • The system demonstrated effectiveness in both daytime and nighttime driving scenarios.

Conclusions:

  • Lane endpoints are viable landmarks for vehicle localization.
  • The developed detection and evaluation methods are suitable for real-world applications.
  • This research contributes to more robust autonomous driving systems.