Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filte...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Baoguo Yu, Hongjuan Zhang, Wenzhuo Li, Chuang Qian, Bijun Li, Chaozhong Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ego-lane index estimation lane-level map particle filter road boundary detection LiDAR GNSS Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/6c02405b5bbf4e81af693c201c3ae934
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6c02405b5bbf4e81af693c201c3ae934
record_format	dspace
spelling	oai:doaj.org-article:6c02405b5bbf4e81af693c201c3ae9342021-11-11T19:07:29ZEgo-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection10.3390/s212171181424-8220https://doaj.org/article/6c02405b5bbf4e81af693c201c3ae9342021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7118https://doaj.org/toc/1424-8220Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.Baoguo YuHongjuan ZhangWenzhuo LiChuang QianBijun LiChaozhong WuMDPI AGarticleego-lane index estimationlane-level mapparticle filterroad boundary detectionLiDARGNSSChemical technologyTP1-1185ENSensors, Vol 21, Iss 7118, p 7118 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ego-lane index estimation lane-level map particle filter road boundary detection LiDAR GNSS Chemical technology TP1-1185
spellingShingle	ego-lane index estimation lane-level map particle filter road boundary detection LiDAR GNSS Chemical technology TP1-1185 Baoguo Yu Hongjuan Zhang Wenzhuo Li Chuang Qian Bijun Li Chaozhong Wu Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection
description	Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.
format	article
author	Baoguo Yu Hongjuan Zhang Wenzhuo Li Chuang Qian Bijun Li Chaozhong Wu
author_facet	Baoguo Yu Hongjuan Zhang Wenzhuo Li Chuang Qian Bijun Li Chaozhong Wu
author_sort	Baoguo Yu
title	Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection
title_short	Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection
title_full	Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection
title_fullStr	Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection
title_full_unstemmed	Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection
title_sort	ego-lane index estimation based on lane-level map and lidar road boundary detection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6c02405b5bbf4e81af693c201c3ae934
work_keys_str_mv	AT baoguoyu egolaneindexestimationbasedonlanelevelmapandlidarroadboundarydetection AT hongjuanzhang egolaneindexestimationbasedonlanelevelmapandlidarroadboundarydetection AT wenzhuoli egolaneindexestimationbasedonlanelevelmapandlidarroadboundarydetection AT chuangqian egolaneindexestimationbasedonlanelevelmapandlidarroadboundarydetection AT bijunli egolaneindexestimationbasedonlanelevelmapandlidarroadboundarydetection AT chaozhongwu egolaneindexestimationbasedonlanelevelmapandlidarroadboundarydetection
_version_	1718431589166219264

Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

Ejemplares similares