Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring

TL;DR

This paper introduces SIWNet, a lightweight deep learning regression model with uncertainty estimation for assessing winter road surface conditions from camera images, enhancing safety and efficiency in autonomous driving.

Contribution

SIWNet is a novel, lightweight regression model that includes a prediction interval estimation mechanism, improving uncertainty quantification in winter road surface condition monitoring.

Findings

Achieved accurate point estimates comparable to state-of-the-art models.

Successfully integrated a prediction interval estimation mechanism.

Model is several times more lightweight, enabling practical deployment.

Abstract

Winter conditions pose several challenges for automated driving applications. A key challenge during winter is accurate assessment of road surface condition, as its impact on friction is a critical parameter for safely and reliably controlling a vehicle. This paper proposes a deep learning regression model, SIWNet, capable of estimating road surface friction properties from camera images. SIWNet extends state of the art by including an uncertainty estimation mechanism in the architecture. This is achieved by including an additional head in the network, which estimates a prediction interval. The prediction interval head is trained with a maximum likelihood loss function. The model was trained and tested with the SeeingThroughFog dataset, which features corresponding road friction sensor readings and images from an instrumented vehicle. Acquired results highlight the functionality of the prediction interval estimation of SIWNet, while the network also achieved similar point estimate accuracy as the previous state of the art. Furthermore, the SIWNet architecture is several times more lightweight than the previously applied state-of-the-art model, resulting in more practical and efficient deployment.