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基于多层激光扫描仪的自动驾驶车辆新型交叉路口类型识别

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

作者信息

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

机构信息

School of Electrical and Electronic Engineering, Yonsei University, 50 Seodaemun-gu Sinchon-dong, Seoul 120-743, Korea.

School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-Ro Dongjak-Gu, Seoul 156-756, Korea.

出版信息

Sensors (Basel). 2016 Jul 20;16(7):1123. doi: 10.3390/s16071123.

DOI:10.3390/s16071123
PMID:27447640
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4970166/
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.

摘要

城市道路中有多种类型的交叉路口，如合流道路、分流道路、十字形交叉路口以及两种类型的T形路口。当自动驾驶车辆遇到新的交叉路口时，识别交叉路口的类型对于安全导航至关重要。本文提出了一种使用多层激光扫描仪的自动驾驶车辆交叉路口类型识别新方法。该方法包括两个步骤：（1）静态局部坐标占用网格地图（SLOGM）构建和（2）交叉路口分类。在第一步中，使用动态二元贝叶斯滤波器相对于局部坐标构建SLOGM。在第二步中，将SLOGM用作分类的属性。该方法应用于实际环境，并通过实验证明了其有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/ca63af701c88/sensors-16-01123-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/437ec3f3054c/sensors-16-01123-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/a49506a3368d/sensors-16-01123-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/e5c90f21a5ab/sensors-16-01123-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/bd15515490bb/sensors-16-01123-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/355a3392cf15/sensors-16-01123-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/d14644ac5fcf/sensors-16-01123-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/0ee86713cdcb/sensors-16-01123-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/728f841f973a/sensors-16-01123-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/aecd509a292b/sensors-16-01123-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/ca63af701c88/sensors-16-01123-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/437ec3f3054c/sensors-16-01123-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/a49506a3368d/sensors-16-01123-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/e5c90f21a5ab/sensors-16-01123-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/bd15515490bb/sensors-16-01123-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/355a3392cf15/sensors-16-01123-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/d14644ac5fcf/sensors-16-01123-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/0ee86713cdcb/sensors-16-01123-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/728f841f973a/sensors-16-01123-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/aecd509a292b/sensors-16-01123-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d63/4970166/ca63af701c88/sensors-16-01123-g010.jpg

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本文引用的文献

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Robust object segmentation using a multi-layer laser scanner.使用多层激光扫描仪进行稳健的目标分割。
Sensors (Basel). 2014 Oct 29;14(11):20400-18. doi: 10.3390/s141120400.
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