# Assessment of Deep Convolutional Neural Networks for Road Surface Classification

arXiv: https://arxiv.org/abs/1804.08872 · HTML: https://mlsanity.com/paper/1804.08872/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 13 of 13)

- [Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection](https://mlsanity.com/paper/2111.10985/) (2021)
- [AGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety](https://mlsanity.com/paper/2410.16999/) (2024)
- [Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring](https://mlsanity.com/paper/2310.00923/) (2023)
- [Road Surface Friction Estimation for Winter Conditions Utilising General Visual Features](https://mlsanity.com/paper/2404.16578/) (2024)
- [Multiple data sources and domain generalization learning method for road surface defect classification](https://mlsanity.com/paper/2407.10197/) (2024)
- [RoadFormer : Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving](https://mlsanity.com/paper/2506.02358/) (2025)
- [Improving classification of road surface conditions via road area extraction and contrastive learning](https://mlsanity.com/paper/2407.14418/) (2024)
- [RoadMamba: A Dual Branch Visual State Space Model for Road Surface Classification](https://mlsanity.com/paper/2508.01210/) (2025)
- [DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification](https://mlsanity.com/paper/2512.06172/) (2025)
- [Exploring Self-Attention for Visual Intersection Classification](https://mlsanity.com/paper/2203.13977/) (2022)
- [Continual Cross-Dataset Adaptation in Road Surface Classification](https://mlsanity.com/paper/2309.02210/) (2023)
- [Fuzzy-Logic and Deep Learning for Environmental Condition-Aware Road Surface Classification](https://mlsanity.com/paper/2512.23436/) (2025)
- [FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2603.19101/) (2026)

## Abstract (arXiv)

When parameterizing vehicle control algorithms for stability or trajectory control, the road-tire friction coefficient is an essential model parameter when it comes to control performance. One major impact on the friction coefficient is the condition of the road surface. A camera-based, forward-looking classification of the road-surface helps enabling an early parametrization of vehicle control algorithms. In this paper, we train and compare two different Deep Convolutional Neural Network models, regarding their application for road friction estimation and describe the challenges for training the classifier in terms of available training data and the construction of suitable datasets.