Self-Supervised Learning of Camera-based Drivable Surface Friction

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

“…This is due the model predicting a continuous value, instead of utilising a discrete number of categories labelled with certain values. Vosahlik et al [4] proposed automated training of a CNN regression model based on corresponding friction information derived from a slip-based contact method. Based on data acquired with a 1:5 scale car model, they created a dataset of matching friction values and images, which included samples from winter conditions.…”

“…A novel CNN architecture, SIWNet, is proposed for the task of computer vision-based estimation of road friction properties. Similarly to the work of Vosahlik et al [4], SIWNet is implemented as a regression model. SIWNet was trained and tested based on an optical road friction sensor ground truth.…”

“…The findings of this paper highlight that computer vision-based winter road surface friction regression is not dependent on an extensively large neural network architecture. Previous research [4] on the topic has applied a relatively large model for the task. This finding is therefore a notable improvement, considering the generally limited vehicle on-board computational resources.…”

“…To focus the analysis on the most relevant portion of the images, the road directly ahead of the vehicle, a predefined static section of all images was cropped and transformed to bird's-eye-view. Similar pre-processing steps were utilised in [21] and [4]. The cropped section was here defined to approximately represent a short section of the lane ahead of the vehicle.…”

“…Furthermore, SIWNet has been designed with practical onboard deployment in mind, with the model being computationally lightweight, yet achieving similar accuracy as previous state of the art. Similarly to [4], SIWNet was trained in an automated manner by matching images and corresponding friction information. However, the work presented here utilised the SeeingThroughFog-dataset [5], where the friction values have been acquired with an optical road friction sensor.…”