A convolutional neural network method to improve efficiency and visualization in modeling driver's visual field on roads using MLS data. (September 2019)

• Record Type: Journal Article
• Authors: Ma, Yang; Zheng, Yubing; Cheng, Jianchuan; Zhang, Yunlong; Han, Wenquan
• Is Part Of: Transportation research. Volume 106(2019)
• Page Start: 317
• Page End: 344
• Publication Date: 2019-09
• Subjects: Viewshed analysis -- Deep learning -- Sight distance -- Efficiency -- Visualization -- MLS data; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.07.018
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Address the driver's visual field (VF) modeling using mobile laser scanning data. Propose a framework that incorporates the convolutional neural network. Model VF nearly 40 times faster than the state-of-the-art method. Provide three manners of data visualization. Abstract: This paper aims to introduce the convolutional neural network (CNN) into modeling driver's visual field (VF) using mobile laser scanning (MLS) data. A new solution that incorporates CNN is proposed to tackle the issues of inefficiency and inadequate manners of visualization in existing methods. The method operates along vehicle trajectory recorded in MLS data. For any driver position, the initial VF is defined as a fan-shaped area originating at the driver's viewpoint. Within the initial VF, numerous virtual line-of-sights (LOS) are emitted from the viewpoint. Given an object point in any LOS, three-dimensional (3D) MLS points that may affect its visibility are converted to two-dimensional (2D) points using the cylindrical perspective projection. 2D points on the projective surface are then transformed into a binary image via the Pixelation procedure. Fed with the generated image, the CNN which is trained based on 789, 500 data will classify the visibility as: 0-visible or 1-invisible. The location of the obstacle that blocks the driver's view along each LOS is detected with a combination of the trained CNN and the bisection method. With all positions of obstacles determined, the final VF is established. Through comparisons with a state-of-the-art method, the CNN-based method shows remarkable efficiency, which facilitates either VF modeling at a single position or successive VF analyses along the vehicle path. A case study is also presented to show the improved manners of data visualization implemented in the developed method: (1) 3D viewshed, (2) sight distance curve, and (3) the driver's perspective image with obstacles spotlighted.