Object Classification of Laser Scanner by Using Recurrent Neural Network

Minho Cho, Jhonghyun An, Wonje Jang, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

These days, laser scanners becomes the primary sensor for advanced driver assistance system (ADAS). The most important theme of ADAS is to distinguish surroundings of egovehicle because notification of situation is the beginning of ADAS such as path planning, mapping and tracking. In this paper, we present approach for object classification by using a laser scanner mounted in vehicle. For object classification, we suggest Recurrent Neural Network (RNN) which is widely used in linguistic study or language model. We rearrange laser scanner data to equivalent theta intervals and apply recurrent neural network model to identify of class about laser scanner point. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments indicate that the proposed method has good performance in real-life situation.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1440-1444
Number of pages5
ISBN (Electronic)9781538654576
DOIs
Publication statusPublished - 2019 Feb 22
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2018-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2018 IEEE Region 10 Conference, TENCON 2018
CountryKorea, Republic of
CityJeju
Period18/10/2818/10/31

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Cho, M., An, J., Jang, W., & Kim, E. (2019). Object Classification of Laser Scanner by Using Recurrent Neural Network. In Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference (pp. 1440-1444). [8650478] (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2018-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2018.8650478