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.

翻译：冬季条件给自动驾驶应用带来了多项挑战。冬季的关键挑战之一是准确评估路面状况，因为其对摩擦力的影响是安全可靠控制车辆的关键参数。本文提出了一种深度学习回归模型SIWNet，能够从相机图像中估计路面摩擦特性。SIWNet通过在其架构中引入不确定性估计机制，超越了现有技术水平。该机制通过在网络中增加一个额外的头部来实现，该头部用于估计预测区间。预测区间头部采用最大似然损失函数进行训练。模型使用SeeingThroughFog数据集进行训练和测试，该数据集包含来自实验车辆的对应路面摩擦传感器读数和图像。获得的结果突显了SIWNet预测区间估计的功能性，同时该网络在点估计精度上也达到了与先前技术水平相当的水平。此外，SIWNet架构比先前应用的最新模型轻量化数倍，从而实现了更实用高效的部署。