# Intelligent Pothole Detection and Road Condition Assessment

arXiv: https://arxiv.org/abs/1710.02595 · HTML: https://mlsanity.com/paper/1710.02595/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [Learning to Automatically Catch Potholes in Worldwide Road Scene Images](https://mlsanity.com/paper/2105.07986/) (2021)
- [Zero-Shot Image-Based Large Language Model Approach to Road Pavement Monitoring](https://mlsanity.com/paper/2504.06785/) (2025)
- [Anomaly Detection in Driving by Cluster Analysis Twice](https://mlsanity.com/paper/2212.07691/) (2022)
- [Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding](https://mlsanity.com/paper/2511.08978/) (2025)
- [Real-time pothole detection with onboard sensors and camera on vehicles](https://mlsanity.com/paper/2511.11643/) (2025)

## Abstract (arXiv)

Poor road conditions are a public nuisance, causing passenger discomfort, damage to vehicles, and accidents. In the U.S., road-related conditions are a factor in 22,000 of the 42,000 traffic fatalities each year. Although we often complain about bad roads, we have no way to detect or report them at scale. To address this issue, we developed a system to detect potholes and assess road conditions in real-time. Our solution is a mobile application that captures data on a car's movement from gyroscope and accelerometer sensors in the phone. To assess roads using this sensor data, we trained SVM models to classify road conditions with 93% accuracy and potholes with 92% accuracy, beating the base rate for both problems. As the user drives, the models use the sensor data to classify whether the road is good or bad, and whether it contains potholes. Then, the classification results are used to create data-rich maps that illustrate road conditions across the city. Our system will empower civic officials to identify and repair damaged roads which inconvenience passengers and cause accidents. This paper details our data science process for collecting training data on real roads, transforming noisy sensor data into useful signals, training and evaluating machine learning models, and deploying those models to production through a real-time classification app. It also highlights how cities can use our system to crowdsource data and deliver road repair resources to areas in need.