Enhanced Winter Road Surface Condition Monitoring with Computer Vision
Risto Ojala Alvari Seppänen
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.