title: Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.
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.
authors.affiliation: School of Electrical and Electronic Engineering, Yonsei University, 50 Seodaemun-gu Sinchon-dong, Seoul 120-743, Korea. jhonghyen@yonsei.ac.kr.
authors.forename: Jhonghyun
authors.initials: J
authors.lastname: An
authors.affiliation: School of Electrical and Electronic Engineering, Yonsei University, 50 Seodaemun-gu Sinchon-dong, Seoul 120-743, Korea. choibae@yonsei.ac.kr.
authors.forename: Baehoon
authors.initials: B
authors.lastname: Choi
authors.affiliation: School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-Ro Dongjak-Gu, Seoul 156-756, Korea. kbsim@cau.ac.kr.
authors.forename: Kwee-Bo
authors.initials: KB
authors.lastname: Sim
authors.affiliation: School of Electrical and Electronic Engineering, Yonsei University, 50 Seodaemun-gu Sinchon-dong, Seoul 120-743, Korea. etkim@yonsei.ac.kr.
authors.forename: Euntai
authors.initials: E
authors.lastname: Kim
keywords: intersections; local coordinate; multi-laser scanner; occupancy grid map; recognition; static map