In:
Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 3 ( 2022-03), p. 342-359
Abstract:
The lane lines’ length, width, and direction are very regular, serialized, and structurally associated, which are not easily affected by the environment. To enhance lane detection in a complicated environment, an approach combines visual information with the spatial distribution. Firstly, the grid density of the target detection algorithm YOLOv3 (you only look once V3) is improved from S×S to S×2S, aiming at the particular points in the bird’s-eye view where the lane lines had different densities in the horizontal and vertical directions. The obtained YOLOv3 (S×2S) is more suitable for detecting objects with small and large aspect ratios. It also identifies image features along with balances the detection speed and accuracy. Secondly, based on a bi-directional gated recurrent unit (BGRU), a new lane line prediction model BGRU-Lane (BGRU-L) based on the distribution of lane lines is proposed using the characteristic of lane line serialization and structural correlation. Finally, Dempster-Shafer (D-S) algorithm based on confidence was used to integrate the results of YOLOv3 (S×2S) and BGRU-L to improve the lane line detection ability under complex environments. The experiment was carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, while Euro Truck Simulator 2 (ETS2) is used as a supplement dataset. After fusing YOLOv3 (S×2S) and BGRU-L models in the D-S model, the detection results have high accuracy in a complex environment by 90.28 mAP. The detection speed is 40.20fps, which enables real-time detection.
Type of Medium:
Online Resource
ISSN:
0361-1981
,
2169-4052
DOI:
10.1177/03611981211051334
Language:
English
Publisher:
SAGE Publications
Publication Date:
2022
detail.hit.zdb_id:
2403378-9
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