A GAN-Augmented CNN Approach for Automated Roadside Safety Assessment of Rural Roadways
Ali Hassandokht Mashhadi, Abbas Rashidi, Nikola Marković
Abstract
The prevalence of run-off-road crashes, particularly in rural areas, underscores the significance of roadside characteristics in safety analysis. This paper proposes a novel approach for automated roadside safety assessment using deep convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs) for data augmentation. The CNN models evaluate roadside features through two-dimensional (2D) image analysis, whereas GANs expand the data set by generating additional diverse samples. The proposed framework aligns with the standard rating system of the Federal Highway Administration (FHWA) and encompasses four distinct models for guardrail detection, clear zone width assessment, rigid obstacle detection, and sideslope estimation. The performance of each model is compared against non-GAN augmented models to assess the efficacy of using GANs for data augmentation. The results show that the proposed approach outperforms existing methods in terms of accuracy, which is measured with 96% in detecting guardrails, 88% in detecting clear zones, 80% in detecting rigid obstacles, and 84% in detecting roadside slopes. Compared with manual approaches, the proposed method offers advantages such as cost-effectiveness, ease of implementation, and the ability to rapidly rank state roads. The developed framework can assist departments of transportation (DOTs) in efficiently identifying problematic road segments and prioritizing safety improvement projects based on FHWA standard rating system.