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...
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7118 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:52:55Z |
| format | Article |
| id | doaj.art-6c02405b5bbf4e81af693c201c3ae934 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:55Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6c02405b5bbf4e81af693c201c3ae9342023-11-22T21:36:53ZengMDPI AGSensors1424-82202021-10-012121711810.3390/s21217118Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary DetectionBaoguo Yu0Hongjuan Zhang1Wenzhuo Li2Chuang Qian3Bijun Li4Chaozhong Wu5The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, ChinaCorrect 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.https://www.mdpi.com/1424-8220/21/21/7118ego-lane index estimationlane-level mapparticle filterroad boundary detectionLiDARGNSS |
| spellingShingle | 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 Sensors ego-lane index estimation lane-level map particle filter road boundary detection LiDAR GNSS |
| title | 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_short | 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 |
| topic | ego-lane index estimation lane-level map particle filter road boundary detection LiDAR GNSS |
| url | https://www.mdpi.com/1424-8220/21/21/7118 |
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