Novel intersection type recognition for autonomous vehicles using a multi-layer laser scanner

Jhonghyun An, Baehoon Choi, Kwee Bo Sim, Euntai Kim

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

Original languageEnglish
Article number1123
JournalSensors (Switzerland)
Volume16
Issue number7
DOIs
Publication statusPublished - 2016 Jul 20

Fingerprint

intersections
scanners
vehicles
Lasers
roads
lasers
grids
T shape
experimentation
navigation
encounters
Navigation
filters

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

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Novel intersection type recognition for autonomous vehicles using a multi-layer laser scanner. / An, Jhonghyun; Choi, Baehoon; Sim, Kwee Bo; Kim, Euntai.

In: Sensors (Switzerland), Vol. 16, No. 7, 1123, 20.07.2016.

Research output: Contribution to journalArticle

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