Lane Lines Detection under Complex Environment by Fusion of Detection and Prediction Models. Issue 3 (March 2022)

• Record Type: Journal Article
• Authors: Haris, Malik; Hou, Jin; Wang, Xiaomin
• Is Part Of: Transportation research record. Volume 2676:Issue 3(2022)
• Page Start: 342
• Page End: 359
• Publication Date: 2022-03
• Subjects: data and data science -- artificial intelligence and advanced computing applications -- machine vision -- urban transportation data and information systems -- data fusion -- infrastructure -- roadway design -- performance effects of geometric design -- lane width; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981211051334
• Languages: English
• ISSNs: 0361-1981

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.