# FARSA: Fully Automated Roadway Safety Assessment

## TL;DR

This paper introduces FARSA, a deep learning system that automates roadway safety ratings from street-level images, significantly reducing manual effort and improving accuracy in safety assessments.

## Contribution

We developed a deep neural network with task-specific attention for automated, rapid, and multi-attribute roadway safety assessment from panoramic images.

## Key findings

- Semi-supervised training reduces overfitting.
- Multi-task learning improves rating accuracy.
- Fast inference per image in milliseconds.

## Abstract

This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadway features in street-level panoramas, a slow and costly process. We propose to automate this process using a deep convolutional neural network that directly estimates the star rating from a street-level panorama, requiring milliseconds per image at test time. Our network also estimates many other road-level attributes, including curvature, roadside hazards, and the type of median. To support this, we incorporate task-specific attention layers so the network can focus on the panorama regions that are most useful for a particular task. We evaluated our approach on a large dataset of real-world images from two US states. We found that incorporating additional tasks, and using a semi-supervised training approach, significantly reduced overfitting problems, allowed us to optimize more layers of the network, and resulted in higher accuracy.