# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 2 Detection Method for Skid Resistance of Asphalt Pavement
### Highway Asphalt Pavement Skid Resistance Detection Method
The acquired images are then uploaded to a computer for subsequent processing. It conducts statistical operations on the elevation point data of the three-dimensional coordinates of the AP surface texture to obtain the evaluation parameters of the surface texture morphology and then evaluates the skid resistance of the AP surface [34]. Compared with the traditional close-up photogrammetry (CRP) method, it works more efficiently and accurately, reduces equipment cost, and lays the foundation for the real-time recognition of texture information and safe braking during the driving of unmanned vehicles, but there is the problem of the uncontrollable loss of texture information [34]. Therefore, it is necessary to improve it to enhance the accuracy and efficiency of texture reconstruction while ensuring the integrity of texture information. The functioning principle of the smart sensor detection method for detecting the skid resistance of AP and the schematic diagram of the smart sensor highway model are shown in Figure 3. The method is based on the prediction model of AP skid resistance established by previous data, and it detects the skid resistance of AP. During this process, it continuously collects, absorbs, trains, optimizes, and integrates the newly detected pavement data to continuously improve the prediction model of AP skid resistance and thus enhance the accuracy of the model. This method is more accurate, easy to measure, less affected by human interference, does not affect traffic, and saves costs. This method increases local economic benefits, but it does not consider the interactions between various weather conditions. And it only considers the information provided by the intelligent road passive sensors. In addition, the less the actual data, the worse the detection effect is, so it requires a large amount of data support [35]. However, with the development of global highways and vehicles, its database will continue to expand, and the accuracy of the model will continue to improve. It is one of the important methods for future AP skid resistance detection. The AP skid resistance testing methods and characteristics are shown in Table 1. Through analyzing different AP skid resistance testing methods, we found that the outdoor testing has the merit of the measured skid data vividly reflecting the actual changes in AP skid resistance [36]. However, its demerit is that the detection is rather lengthy, labor-intensive, and necessitates traffic closure. Generally, to stabilize the pavement's skid resistance, it usually takes 3-5 years of vehicle loading. Thus, regarding the detection cycle, there are drawbacks like a prolonged cycle, disruption to normal traffic, high costs, unobvious data patterns, and numerous uncontrollable elements [37]. In addition to the continuous development of equipment and technology for obtaining AP texture, most of the studies at home and abroad have only calculated the MTD indicator after obtaining the pavement texture information, which is the most used indicator for characterizing the skid resistance of pavement after non-contact, obtaining the AP texture information [38; 39; 40]. Compared with the sand spreading method, the MTD has higher credibility and reliability in characterizing the skid resistance of the pavement. However, it cannot be denied that the correlation between the MTD and the friction coefficient is rather weak, and a strong correlation can only be observed within a specific range, the texture structure and distribution of the pavement are more complex, and only one MTD indicator fails to comprehensively reflect the pavement texture information and skid resistance, so the subsequent study should use multiple indicators to characterize the texture and skid resistance of the AP in multiple dimensions.