Risk map generation system for intelligent vehicles on community roads via data-driven approach

The aim of this study is to develop a risk map generation system on community roads via a data-driven approach. Because there are no roadside cooperative sensing systems on community roads, intelligent motion controls need the prediction of surrounding traffic participants based on a digital map. In...

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Autores principales:	Takuma ITO, Masatsugu SOYA, Kyoichi TOHRIYAMA, Minoru KAMATA
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2020
Materias:	automobile digital map driving behavior data driven analysis map deepening Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/af40f721e92546d58af365d6ce1e4fbe
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id	oai:doaj.org-article:af40f721e92546d58af365d6ce1e4fbe
record_format	dspace
spelling	oai:doaj.org-article:af40f721e92546d58af365d6ce1e4fbe2021-11-29T05:52:03ZRisk map generation system for intelligent vehicles on community roads via data-driven approach2187-974510.1299/mej.19-00119https://doaj.org/article/af40f721e92546d58af365d6ce1e4fbe2020-02-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/7/1/7_19-00119/_pdf/-char/enhttps://doaj.org/toc/2187-9745The aim of this study is to develop a risk map generation system on community roads via a data-driven approach. Because there are no roadside cooperative sensing systems on community roads, intelligent motion controls need the prediction of surrounding traffic participants based on a digital map. In addition, to improve the driver acceptance of the system, adaptive motion control considering the risk level at various intersections is desirable. In general, existing risk map preparation methods need too many human resources to prepare the map data for nationwide community roads. To solve this problem, we design a risk map generation system that can be systemized as much as possible for future automated process. First, our proposed system collects the driving data via non-specialized drivers using relatively usual vehicles at various intersections. Next, the system classifies the driving behavior via feature values with regard to the deceleration operation while approaching non-signalized intersections. Based on the driving data of relatively careful drivers, the system extracts the positions of pseudo intersections that are not registered in the digital map. In addition, the system estimates the risk level of intersections based on the driving data of relatively interactive drivers. To confirm the feasibility of the proposed system, we collect the driving data of 45 elderly drivers. According to the evaluation, the system adequately detects the pseudo intersections on actual roads. The estimation results of the risk level show the substantial agreement with the risk evaluations of driving school instructors regarding basic intersections.Takuma ITOMasatsugu SOYAKyoichi TOHRIYAMAMinoru KAMATAThe Japan Society of Mechanical Engineersarticleautomobiledigital mapdriving behaviordata driven analysismap deepeningMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 7, Iss 1, Pp 19-00119-19-00119 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	automobile digital map driving behavior data driven analysis map deepening Mechanical engineering and machinery TJ1-1570
spellingShingle	automobile digital map driving behavior data driven analysis map deepening Mechanical engineering and machinery TJ1-1570 Takuma ITO Masatsugu SOYA Kyoichi TOHRIYAMA Minoru KAMATA Risk map generation system for intelligent vehicles on community roads via data-driven approach
description	The aim of this study is to develop a risk map generation system on community roads via a data-driven approach. Because there are no roadside cooperative sensing systems on community roads, intelligent motion controls need the prediction of surrounding traffic participants based on a digital map. In addition, to improve the driver acceptance of the system, adaptive motion control considering the risk level at various intersections is desirable. In general, existing risk map preparation methods need too many human resources to prepare the map data for nationwide community roads. To solve this problem, we design a risk map generation system that can be systemized as much as possible for future automated process. First, our proposed system collects the driving data via non-specialized drivers using relatively usual vehicles at various intersections. Next, the system classifies the driving behavior via feature values with regard to the deceleration operation while approaching non-signalized intersections. Based on the driving data of relatively careful drivers, the system extracts the positions of pseudo intersections that are not registered in the digital map. In addition, the system estimates the risk level of intersections based on the driving data of relatively interactive drivers. To confirm the feasibility of the proposed system, we collect the driving data of 45 elderly drivers. According to the evaluation, the system adequately detects the pseudo intersections on actual roads. The estimation results of the risk level show the substantial agreement with the risk evaluations of driving school instructors regarding basic intersections.
format	article
author	Takuma ITO Masatsugu SOYA Kyoichi TOHRIYAMA Minoru KAMATA
author_facet	Takuma ITO Masatsugu SOYA Kyoichi TOHRIYAMA Minoru KAMATA
author_sort	Takuma ITO
title	Risk map generation system for intelligent vehicles on community roads via data-driven approach
title_short	Risk map generation system for intelligent vehicles on community roads via data-driven approach
title_full	Risk map generation system for intelligent vehicles on community roads via data-driven approach
title_fullStr	Risk map generation system for intelligent vehicles on community roads via data-driven approach
title_full_unstemmed	Risk map generation system for intelligent vehicles on community roads via data-driven approach
title_sort	risk map generation system for intelligent vehicles on community roads via data-driven approach
publisher	The Japan Society of Mechanical Engineers
publishDate	2020
url	https://doaj.org/article/af40f721e92546d58af365d6ce1e4fbe
work_keys_str_mv	AT takumaito riskmapgenerationsystemforintelligentvehiclesoncommunityroadsviadatadrivenapproach AT masatsugusoya riskmapgenerationsystemforintelligentvehiclesoncommunityroadsviadatadrivenapproach AT kyoichitohriyama riskmapgenerationsystemforintelligentvehiclesoncommunityroadsviadatadrivenapproach AT minorukamata riskmapgenerationsystemforintelligentvehiclesoncommunityroadsviadatadrivenapproach
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Risk map generation system for intelligent vehicles on community roads via data-driven approach

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