# FARSA: Fully Automated Roadway Safety Assessment

arXiv: https://arxiv.org/abs/1901.06013 · HTML: https://mlsanity.com/paper/1901.06013/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?](https://mlsanity.com/paper/2408.10872/) (2024)
- [Explainable, automated urban interventions to improve pedestrian and vehicle safety](https://mlsanity.com/paper/2110.11672/) (2021)
- [Handling Image and Label Resolution Mismatch in Remote Sensing](https://mlsanity.com/paper/2211.15790/) (2022)
- [Beta Distribution Learning for Reliable Roadway Crash Risk Assessment](https://mlsanity.com/paper/2511.04886/) (2025)
- [Predicting the impact of urban change in pedestrian and road safety](https://mlsanity.com/paper/2202.01781/) (2022)
- [Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction](https://mlsanity.com/paper/2605.24040/) (2026)
- [EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings](https://mlsanity.com/paper/2608.23563/) (2026)

## Abstract (arXiv)

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.