Article: CNN-based lane-level positioning with only on-board camera Journal: International Journal of Sensor Networks (IJSNET) 2025 Vol.47 No.1 pp.1 - 10 Abstract: Lane-level positioning is a vital prerequisite for realising autonomous driving in complex scenarios. Existing methods for lane-level positioning mostly rely on the global positioning system (GPS) and vision-based approaches. Although the positioning accuracy of civil GPS can reach up to metre-level in an environment with good signal, it is hardly to meet the precision requirement for the lane-level vehicle's positioning in which centimetre-level precision is desired. Some traditional vision-based methods can achieve decimetre-level accuracy, they usually suffer from the weakness of low detection speed and the difficulty in handling multi-lane detection tasks. This paper proposes a one-camera low-cost approach that utilises convolutional neural network (CNN)-based segmentation for lane detection and traditional image processing techniques for lane determination. The effectiveness and robustness of the proposed approach have been tested and verified on the widely-used dataset TuSimple. It is shown that our method can achieve high detection speed while maintaining a certain detection accuracy. Inderscience Publishers - linking academia, business and industry through research

Title: CNN-based lane-level positioning with only on-board camera

Authors: Chen Li; Zhengqiong Liu; Momiao Zhou; Yanshi Sun; Zhizhong Ding

Addresses: School of Computer and Information, Hefei University of Technology, Hefei, 230009, China ' School of Computer and Information, Hefei University of Technology, Hefei, 230009, China ' School of Computer and Information, Hefei University of Technology, Hefei, 230009, China ' School of Computer and Information, Hefei University of Technology, Hefei, 230009, China ' School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

Abstract: Lane-level positioning is a vital prerequisite for realising autonomous driving in complex scenarios. Existing methods for lane-level positioning mostly rely on the global positioning system (GPS) and vision-based approaches. Although the positioning accuracy of civil GPS can reach up to metre-level in an environment with good signal, it is hardly to meet the precision requirement for the lane-level vehicle's positioning in which centimetre-level precision is desired. Some traditional vision-based methods can achieve decimetre-level accuracy, they usually suffer from the weakness of low detection speed and the difficulty in handling multi-lane detection tasks. This paper proposes a one-camera low-cost approach that utilises convolutional neural network (CNN)-based segmentation for lane detection and traditional image processing techniques for lane determination. The effectiveness and robustness of the proposed approach have been tested and verified on the widely-used dataset TuSimple. It is shown that our method can achieve high detection speed while maintaining a certain detection accuracy.

Keywords: lane-level vehicle's positioning; lane detection; CNN; instance segmentation.

DOI: 10.1504/IJSNET.2025.143903

International Journal of Sensor Networks, 2025 Vol.47 No.1, pp.1 - 10

Received: 10 May 2023
Accepted: 14 Apr 2024
Published online: 13 Jan 2025 *

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Title: CNN-based lane-level positioning with only on-board camera

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