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 distinctive attributes of the geometry, such as visual curvature, slope, sight distance, and curve direction. The latent parameters provided quantitative measurements of how the design scheme looks like in perspective view. It was found that a road with low accident rate has low values for codes 4 and 5, high values for code 3, and low variance for codes 3 and 6. The trained VAE model also ensured accurate generation of the perspective images by decoding the latent parameters. Overall, this research advances the understanding of road design by considering the driver's perception.

中文翻译：

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使用变体自动编码器从驾驶员角度对道路几何进行建模和解释

由于几何形状的复杂性，对透视几何的定量描述是一项艰巨的任务。在本文中，我们通过提出一种基于变分自动编码器（VAE）的新方法来得出透视道路几何形状的低维和可利用参数的方法来解决这一差距。首先，根据不同的对齐方案生成道路透视图图像。然后，构建了一个VAE，以根据数据创建可规范化和可利用的潜在空间。潜在空间是透视几何体的压缩表示形式，从中可以导出六个潜在参数。没有事先的专业知识，就发现其中四个潜在参数代表了几何的独特属性，例如视觉曲率，斜率，视距和曲线方向。潜在参数提供了对设计方案在透视图中的外观的定量测量。已发现事故率低的道路的代码4和5的值较低，代码3的值较高，代码3和6的方差很小。经过训练的VAE模型还通过解码潜伏来确保准确生成透视图。参数。总体而言，这项研究通过考虑驾驶员的感知来增进对道路设计的理解。