The pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction mechanisms and deep neural networks has not been fully studied. This study employed spatial‐channel attention mechanisms to integrate frictional domain knowledge and convolutional neural networks (CNNs) that predict the friction coefficient as the output. The models’ inputs include 3D texture data, corresponding finite element (FE) simulation outcomes, and 2D wavelet decomposition outcomes. An additional spatial attention (ASA) mechanism guided the CNNs to focus on the tire–road contact region, using tire–road contact stress from FE simulation as domain knowledge. Multi‐scale channel attention (MSCA) mechanisms enabled the CNNs to learn the channel weights of 2D‐wavelet‐based multi‐scale inputs, thereby assessing the contribution of different texture scales to tire–road friction. A multi‐attention and feature fusion mechanism was designed, and the performances of various combinations were compared. The results showed that the fusion of ASA and MSCA achieved the best performance, with a regression R2 of 0.8470, which was a 20.25% improvement over the baseline model.

中文翻译：

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将空间和通道注意力机制与卷积神经网络中的领域知识相结合，用于摩擦系数预测


路面防滑性对于确保驾驶安全至关重要。然而，现场测量的可重复性和可比性受到各种影响因素的限制。解决这些限制的一种方法是利用基于激光的 3D 路面数据，这些数据非常稳定，可用于间接估计路面防滑性。然而，轮胎-路面摩擦机制和深度神经网络的集成尚未得到充分研究。本研究采用空间通道注意力机制，将摩擦域知识和预测摩擦系数作为输出的卷积神经网络 （CNN） 集成在一起。模型的输入包括 3D 纹理数据、相应的有限元 （FE） 仿真结果和 2D 小波分解结果。一种额外的空间注意力 （ASA） 机制指导 CNN 专注于轮胎-道路接触区域，使用来自 FE 仿真的轮胎-道路接触应力作为领域知识。多尺度通道注意力 （MSCA） 机制使 CNN 能够学习基于 2D 小波的多尺度输入的通道权重，从而评估不同纹理尺度对轮胎-道路摩擦的贡献。设计了一种多注意力和特征融合机制，并比较了各种组合的性能。结果表明，ASA 和 MSCA 的融合取得了最佳性能，回归 R2 为 0.8470，比基线模型提高了 20.25%。