A GAN-Augmented CNN Approach for Automated Roadside Safety Assessment of Rural Roadways

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.

This research focuses on the development of computer vision models for roadside safety assessment. The models successfully detect and classify important features such as guardrails, clear zones, rigid obstacles, and roadside slopes in images. The practical applications of this research are significant for transportation authorities, engineers, and practitioners involved in roadway safety. By utilizing these computer vision models, they can efficiently analyze large amounts of visual data and identify potential safety hazards along roadways. These models can aid in identifying areas with inadequate clear zones, which are crucial for preventing roadside crashes. They can also detect the presence of guardrails, which play a vital role in redirecting vehicles and minimizing the severity of accidents. Moreover, the models provide insights into the presence and distance of rigid obstacles, such as trees, rocks, and mountains, which are essential for assessing the overall safety of roadside environments. Last, the models assess roadside slopes, allowing practitioners to identify areas with steep inclines that may pose a higher risk to motorists. Overall, the practical applications of these computer vision models enable stakeholders to prioritize safety interventions, implement targeted improvements, and enhance roadway safety for both drivers and pedestrians.