Journal article

Modeling and interpreting road geometry from a driver's perspective using variational autoencoders

Fan Wang, Yuren Chen, Jasper S Wijnands, Jingqiu Guo

Computer-Aided Civil and Infrastructure Engineering | WILEY | Published : 2020

Abstract

Quantitative description of perspective geometries is a challenging task due to the complexities of geometric shapes. In this paper, we address this gap by proposing a new methodology based on variational autoencoders (VAE) to derive low-dimensional and exploitable parameters of the perspective road geometry. First, road perspective images were generated based on different alignment scenarios. Then, a VAE was built to create a regularized and exploitable latent space from the data. The latent space is a compressed representation of perspective geometry, from which six latent parameters were derived. Without prior expert knowledge, four of the latent parameters were found to represent distinc..

View full abstract