## 1 Introduction
After completing the pavement distress detection, the calculation results eventually need to be transformed into the detailed distress types and the geometric parameters of the distress [45], and then the PCI can be calculated [46]. It should be noted that, compared with research on the semantic segmentation of pavement distress, there have been relatively few quantitative studies on the segmentation results. Although a few crack quantization methods have realized the calculation of the width, length, and other eigenvalues of the cracks, the computation is time-consuming, and the results of the computation are affected by the impact of the complex scenario. For example, [PERSON] et al. [4] performed crack classification and width calculation based on the segmentation results but failed to realize quantitative evaluation of the pavement condition. In addition, no research on pavement damage conditions based on quantitative features has been seen to date [47]. The main indices currently used to characterize the pavement condition include the pavement damage condition index (PCI), the existing pavement functional index (the present serviceability index, PSI), the present serviceability rating (PSR), and the pavement rating (pavement surface evaluation and rating (PASER)) [48]. Among these indices, the PCI is a quantitative pavement condition-based index, and all the other indicators can be categorized as qualitative pavement indices. Moreover, the PCI was developed by the U.S. Army Corps of Engineers, based on a large amount of measured inspection data and visual observations, and it is currently the most commonly used pave ment condition assessment index in the systems of highway pavement rehabilitation and maintenance management [49]. Although some research teams have proposed the use of neural network algorithms [50], decision tree algorithms [51], and integrated ANN and genetic programming (GP) algorithms [52] to predict pavement conditions, the limitation of these evaluation models is that they all rely on manual inspection of the damage. The inspector needs to calculate the area, length, and severity of the damage, and the outputs can then be used as inputs to these models to calculate the PCI. Therefore, there is a need for a fully automated pavement condition evaluation model, based on the output results of the pavement distress inspection models. To address the abovementioned issues of multi-type pavement distress segmentation and automated prediction of pavement conditions in complex scenarios, based on the ISTD-PDS7 [20] proposed in our previous research, we first proposed a network based on a hierarchical transformer structure (named ISTD-DisNet) to extract the pavement distresses in an end-to-end manner. ISTD-DisNet adopts a hierarchical transformer encoder structure without positional coding in SegFormer to take into account the long-range contextual information on pavement distress at different scales. A mixed attention module (MAM) was introduced at the decoding stage to attenuate or even eliminate the interference of irrelevant information and enhance the representation of distress features in complex contexts; meanwhile, a transposed convolution upsampling module (TCUM) was constructed using transposed convolution with a learning capability, aiming to enhance the model's ability to restore details. The main contributions of this study are summarized below:  
1. To enhance the model's multi-scale distress feature representation and detail recovery while modeling long-range dependencies and providing a global feature representation, we designed a multi-scale distress segmentation network--ISTD-DisNet--with an encoder-decoder transformer architecture. 2. Based on the ISTD-DisNet output, we analyzed the correlation between the _PDR_ and PCI indices and developed a new pavement damage condition prediction model. 3.