Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels
The satellite navigation signal in the tunnel is weak, and it is difficult to achieve accurate lateral positioning in complex conditions such as low-speed congestion by relying solely on inertial navigation or line image recognition, which is one of the problems of automatic driving at present. In t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6634 |
| _version_ | 1797480873199665152 |
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| author | Xuedong Jiang Zunmin Liu Bilong Liu Jiang Liu |
| author_facet | Xuedong Jiang Zunmin Liu Bilong Liu Jiang Liu |
| author_sort | Xuedong Jiang |
| collection | DOAJ |
| description | The satellite navigation signal in the tunnel is weak, and it is difficult to achieve accurate lateral positioning in complex conditions such as low-speed congestion by relying solely on inertial navigation or line image recognition, which is one of the problems of automatic driving at present. In this paper, a lane-level location method based on multi-sensor fusion is proposed. Using the machine vision method, detecting lane lines with the monocular camera, and fitting the lane lines to determine the driving status of the vehicle based on the lane line information. The top view of the lane line is taken by the binocular camera, and the distance of the vehicle from the lane line and the width of the lane are calculated from the pictures taken by the binocular camera. Obtaining the heading angle information of the vehicle using the gyroscope in inertial navigation and the distance information of the vehicle using the odometer. When a car changes lanes or overtakes, the new lane the vehicle is in is calculated by calculating the difference in heading angle and combining it with the lane width and odometer information so as to complete the lateral positioning of the vehicle. The simulation results show that the algorithm has high lateral positioning accuracy. The positioning accuracy is less affected by the drift of inertial elements, and the error will not accumulate. |
| first_indexed | 2024-03-09T22:06:27Z |
| format | Article |
| id | doaj.art-5031de56b475400db825fb0ca2681444 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:27Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5031de56b475400db825fb0ca26814442023-11-23T19:40:16ZengMDPI AGApplied Sciences2076-34172022-06-011213663410.3390/app12136634Multi-Sensor Fusion for Lateral Vehicle Localization in TunnelsXuedong Jiang0Zunmin Liu1Bilong Liu2Jiang Liu3School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaThe satellite navigation signal in the tunnel is weak, and it is difficult to achieve accurate lateral positioning in complex conditions such as low-speed congestion by relying solely on inertial navigation or line image recognition, which is one of the problems of automatic driving at present. In this paper, a lane-level location method based on multi-sensor fusion is proposed. Using the machine vision method, detecting lane lines with the monocular camera, and fitting the lane lines to determine the driving status of the vehicle based on the lane line information. The top view of the lane line is taken by the binocular camera, and the distance of the vehicle from the lane line and the width of the lane are calculated from the pictures taken by the binocular camera. Obtaining the heading angle information of the vehicle using the gyroscope in inertial navigation and the distance information of the vehicle using the odometer. When a car changes lanes or overtakes, the new lane the vehicle is in is calculated by calculating the difference in heading angle and combining it with the lane width and odometer information so as to complete the lateral positioning of the vehicle. The simulation results show that the algorithm has high lateral positioning accuracy. The positioning accuracy is less affected by the drift of inertial elements, and the error will not accumulate.https://www.mdpi.com/2076-3417/12/13/6634vehicle locationinertial navigationmonocular camerasbinocular camera |
| spellingShingle | Xuedong Jiang Zunmin Liu Bilong Liu Jiang Liu Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels Applied Sciences vehicle location inertial navigation monocular cameras binocular camera |
| title | Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels |
| title_full | Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels |
| title_fullStr | Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels |
| title_full_unstemmed | Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels |
| title_short | Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels |
| title_sort | multi sensor fusion for lateral vehicle localization in tunnels |
| topic | vehicle location inertial navigation monocular cameras binocular camera |
| url | https://www.mdpi.com/2076-3417/12/13/6634 |
| work_keys_str_mv | AT xuedongjiang multisensorfusionforlateralvehiclelocalizationintunnels AT zunminliu multisensorfusionforlateralvehiclelocalizationintunnels AT bilongliu multisensorfusionforlateralvehiclelocalizationintunnels AT jiangliu multisensorfusionforlateralvehiclelocalizationintunnels |