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

arXiv:2310.00923v2 Announce Type: replace
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 …

abstract applications arxiv assessment automated challenge challenges computer computer vision cs.cv driving impact interval key monitoring paper prediction regression surface type vision