Fuzzy-Logic and Deep Learning for Environmental Condition-Aware Road Surface Classification

TL;DR

This paper presents a real-time, image and acceleration data-driven system for classifying road surface conditions with high accuracy, integrating deep learning and fuzzy logic to account for weather and time variations.

Contribution

It introduces a novel combination of deep learning and fuzzy logic for environmental-aware road surface classification using mobile data.

Findings

Over 95% accuracy in classifying six road surface types

Deep learning algorithms like AlexNet, LeNet, VGG, ResNet tested for performance

Fuzzy logic approach for weather and time-based classification proposed

Abstract

Monitoring states of road surfaces provides valuable information for the planning and controlling vehicles and active vehicle control systems. Classical road monitoring methods are expensive and unsystematic because they require time for measurements. This article proposes an real time system based on weather conditional data and road surface condition data. For this purpose, we collected data with a mobile phone camera on the roads around the campus of the Karlsruhe Institute of Technology. We tested a large number of different image-based deep learning algorithms for road classification. In addition, we used road acceleration data along with road image data for training by using them as images. We compared the performances of acceleration-based and camera image-based approaches. The performances of the simple Alexnet, LeNet, VGG, and Resnet algorithms were compared as deep learning algorithms. For road condition classification, 5 classes were considered: asphalt, damaged asphalt, gravel road, damaged gravel road, pavement road and over 95% accuracy performance was achieved. It is also proposed to use the acceleration or the camera image to classify the road surface according to the weather and the time of day using fuzzy logic.