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Author:

Duan, Jianmin (Duan, Jianmin.) (Scholars:段建民) | Shi, Lixiao (Shi, Lixiao.) | Yao, Junqin (Yao, Junqin.) | Liu, Dan (Liu, Dan.) | Tian, Qi (Tian, Qi.)

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EI Scopus

Abstract:

In order to complete the task of vehicle's autonomous driving in structural road, it's necessary to detect obstacles. This paper proposed a method of obstacle detection, taking use of the four-line laser radar. This algorithm combined the improved DBSCAN with K-Means and overcame DBSCAN's defect that it couldn't divide obstacles with similar density. At the same time, this method can eliminate noise points effectively. Based on clustering, obstacle's information can be acquired, such as angle, distance and size, to achieve the task of obstacle detection. The algorithm is applied to obstacle detection in intelligent vehicle. The test proved that it is consistent and reliable, which complies with the requirement of intelligent vehicle's autonomous driving. © 2013 IEEE.

Keyword:

Intelligent vehicle highway systems Obstacle detectors Autonomous vehicles Optical radar Robotics K-means clustering Biomimetics Tracking radar

Author Community:

  • [ 1 ] [Duan, Jianmin]Measurement-Control System and Equipment Group, Department of Control Science and Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 2 ] [Shi, Lixiao]Measurement-Control System and Equipment Group, Department of Control Science and Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Yao, Junqin]Measurement-Control System and Equipment Group, Department of Control Science and Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Liu, Dan]Measurement-Control System and Equipment Group, Department of Control Science and Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 5 ] [Tian, Qi]Measurement-Control System and Equipment Group, Department of Control Science and Engineering, Beijing University of Technology, Beijing 100124, China

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Year: 2013

Page: 2452-2457

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 11

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