Loading…

A Forward Obstacle Detection Approach for Trains Based on 4D Radar

With the expansion of urban scale, rail transport has become an indispensable component of the city's public transportation. Achieving intelligent assisted driving in rail transport can reduce incidents due to driver factors. Sensors enabling accurate perception of the surrounding environment a...

Full description

Saved in:
Bibliographic Details
Main Authors: Wang, Dajing, Liu, Quanli, Wang, Wei, Yu, Zichen, Liu, Xin
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1432
container_issue
container_start_page 1426
container_title
container_volume
creator Wang, Dajing
Liu, Quanli
Wang, Wei
Yu, Zichen
Liu, Xin
description With the expansion of urban scale, rail transport has become an indispensable component of the city's public transportation. Achieving intelligent assisted driving in rail transport can reduce incidents due to driver factors. Sensors enabling accurate perception of the surrounding environment are the prerequisite for intelligent assisted driving. 4D Radar generates point clouds that include both 3D position and velocity information. Especially, 4D Radar works well in bad weather conditions compared to Lidar and camera. This paper proposes an obstacle detection method based on 4D Radar in the train-travelling environment, which dynamically adjusts the obstacle detection range using DBSCAN. Moreover, the network is improved for the characteristic of sparse 4D Radar point cloud, and enhancing the detection capability of the network through the introduction of an attention module. Experimental results show that the method achieves satisfactory results in real scenarios and has great potential.
doi_str_mv 10.1109/YAC63405.2024.10598410
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_10598410</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10598410</ieee_id><sourcerecordid>10598410</sourcerecordid><originalsourceid>FETCH-ieee_primary_105984103</originalsourceid><addsrcrecordid>eNqFzrsKwjAUgOEoCIr2DUTOC1hPkl6SsRfFTZAuTnJsI1ZqW5KC-PY66Oz0D9_yM7bi6HOOenNKskgGGPoCReBzDLUKOI6Yp2OtZIgy1kKoMZsJJeO1ipBPmefcHRElV5rHOGNpArvOPslWcLi4gcrGQG4GUw5110LS97aj8gbXzkJhqW4dpORMBR8McjhSRXbBJldqnPG-nbPlbltk-3VtjDn3tn6QfZ1_e_IPvwHtpTvC</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A Forward Obstacle Detection Approach for Trains Based on 4D Radar</title><source>IEEE Xplore All Conference Series</source><creator>Wang, Dajing ; Liu, Quanli ; Wang, Wei ; Yu, Zichen ; Liu, Xin</creator><creatorcontrib>Wang, Dajing ; Liu, Quanli ; Wang, Wei ; Yu, Zichen ; Liu, Xin</creatorcontrib><description>With the expansion of urban scale, rail transport has become an indispensable component of the city's public transportation. Achieving intelligent assisted driving in rail transport can reduce incidents due to driver factors. Sensors enabling accurate perception of the surrounding environment are the prerequisite for intelligent assisted driving. 4D Radar generates point clouds that include both 3D position and velocity information. Especially, 4D Radar works well in bad weather conditions compared to Lidar and camera. This paper proposes an obstacle detection method based on 4D Radar in the train-travelling environment, which dynamically adjusts the obstacle detection range using DBSCAN. Moreover, the network is improved for the characteristic of sparse 4D Radar point cloud, and enhancing the detection capability of the network through the introduction of an attention module. Experimental results show that the method achieves satisfactory results in real scenarios and has great potential.</description><identifier>EISSN: 2837-8601</identifier><identifier>EISBN: 9798350379228</identifier><identifier>DOI: 10.1109/YAC63405.2024.10598410</identifier><language>eng</language><publisher>IEEE</publisher><subject>4D Radar point clouds ; Accuracy ; intelligent train ; Point cloud compression ; Radar ; Radar detection ; Radar tracking ; Rails ; railway obstacle detection ; railway safety ; Three-dimensional displays</subject><ispartof>2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2024, p.1426-1432</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10598410$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10598410$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Dajing</creatorcontrib><creatorcontrib>Liu, Quanli</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Yu, Zichen</creatorcontrib><creatorcontrib>Liu, Xin</creatorcontrib><title>A Forward Obstacle Detection Approach for Trains Based on 4D Radar</title><title>2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)</title><addtitle>YAC</addtitle><description>With the expansion of urban scale, rail transport has become an indispensable component of the city's public transportation. Achieving intelligent assisted driving in rail transport can reduce incidents due to driver factors. Sensors enabling accurate perception of the surrounding environment are the prerequisite for intelligent assisted driving. 4D Radar generates point clouds that include both 3D position and velocity information. Especially, 4D Radar works well in bad weather conditions compared to Lidar and camera. This paper proposes an obstacle detection method based on 4D Radar in the train-travelling environment, which dynamically adjusts the obstacle detection range using DBSCAN. Moreover, the network is improved for the characteristic of sparse 4D Radar point cloud, and enhancing the detection capability of the network through the introduction of an attention module. Experimental results show that the method achieves satisfactory results in real scenarios and has great potential.</description><subject>4D Radar point clouds</subject><subject>Accuracy</subject><subject>intelligent train</subject><subject>Point cloud compression</subject><subject>Radar</subject><subject>Radar detection</subject><subject>Radar tracking</subject><subject>Rails</subject><subject>railway obstacle detection</subject><subject>railway safety</subject><subject>Three-dimensional displays</subject><issn>2837-8601</issn><isbn>9798350379228</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNqFzrsKwjAUgOEoCIr2DUTOC1hPkl6SsRfFTZAuTnJsI1ZqW5KC-PY66Oz0D9_yM7bi6HOOenNKskgGGPoCReBzDLUKOI6Yp2OtZIgy1kKoMZsJJeO1ipBPmefcHRElV5rHOGNpArvOPslWcLi4gcrGQG4GUw5110LS97aj8gbXzkJhqW4dpORMBR8McjhSRXbBJldqnPG-nbPlbltk-3VtjDn3tn6QfZ1_e_IPvwHtpTvC</recordid><startdate>20240607</startdate><enddate>20240607</enddate><creator>Wang, Dajing</creator><creator>Liu, Quanli</creator><creator>Wang, Wei</creator><creator>Yu, Zichen</creator><creator>Liu, Xin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20240607</creationdate><title>A Forward Obstacle Detection Approach for Trains Based on 4D Radar</title><author>Wang, Dajing ; Liu, Quanli ; Wang, Wei ; Yu, Zichen ; Liu, Xin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_105984103</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>4D Radar point clouds</topic><topic>Accuracy</topic><topic>intelligent train</topic><topic>Point cloud compression</topic><topic>Radar</topic><topic>Radar detection</topic><topic>Radar tracking</topic><topic>Rails</topic><topic>railway obstacle detection</topic><topic>railway safety</topic><topic>Three-dimensional displays</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Dajing</creatorcontrib><creatorcontrib>Liu, Quanli</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Yu, Zichen</creatorcontrib><creatorcontrib>Liu, Xin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Dajing</au><au>Liu, Quanli</au><au>Wang, Wei</au><au>Yu, Zichen</au><au>Liu, Xin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Forward Obstacle Detection Approach for Trains Based on 4D Radar</atitle><btitle>2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)</btitle><stitle>YAC</stitle><date>2024-06-07</date><risdate>2024</risdate><spage>1426</spage><epage>1432</epage><pages>1426-1432</pages><eissn>2837-8601</eissn><eisbn>9798350379228</eisbn><abstract>With the expansion of urban scale, rail transport has become an indispensable component of the city's public transportation. Achieving intelligent assisted driving in rail transport can reduce incidents due to driver factors. Sensors enabling accurate perception of the surrounding environment are the prerequisite for intelligent assisted driving. 4D Radar generates point clouds that include both 3D position and velocity information. Especially, 4D Radar works well in bad weather conditions compared to Lidar and camera. This paper proposes an obstacle detection method based on 4D Radar in the train-travelling environment, which dynamically adjusts the obstacle detection range using DBSCAN. Moreover, the network is improved for the characteristic of sparse 4D Radar point cloud, and enhancing the detection capability of the network through the introduction of an attention module. Experimental results show that the method achieves satisfactory results in real scenarios and has great potential.</abstract><pub>IEEE</pub><doi>10.1109/YAC63405.2024.10598410</doi></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2837-8601
ispartof 2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2024, p.1426-1432
issn 2837-8601
language eng
recordid cdi_ieee_primary_10598410
source IEEE Xplore All Conference Series
subjects 4D Radar point clouds
Accuracy
intelligent train
Point cloud compression
Radar
Radar detection
Radar tracking
Rails
railway obstacle detection
railway safety
Three-dimensional displays
title A Forward Obstacle Detection Approach for Trains Based on 4D Radar
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T16%3A09%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20Forward%20Obstacle%20Detection%20Approach%20for%20Trains%20Based%20on%204D%20Radar&rft.btitle=2024%2039th%20Youth%20Academic%20Annual%20Conference%20of%20Chinese%20Association%20of%20Automation%20(YAC)&rft.au=Wang,%20Dajing&rft.date=2024-06-07&rft.spage=1426&rft.epage=1432&rft.pages=1426-1432&rft.eissn=2837-8601&rft_id=info:doi/10.1109/YAC63405.2024.10598410&rft.eisbn=9798350379228&rft_dat=%3Cieee_CHZPO%3E10598410%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-ieee_primary_105984103%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10598410&rfr_iscdi=true