To explore the influence of the shape of corrugated steel plate (CSP) on the reinforced concrete (RC) slab culvert rehabilitated with grouted CSP, and the discrimination of arch effect of grout, this paper established 72 numerical models of the rehabilitated system with box, arc and transition shapes of CSPs by combining laboratory test with numerical analysis, based on the laboratory experimental results. The variation law of the load-carrying capacity of the rehabilitated system changed with the shape parameters of CSPs was ascertained. In addition, based on the concept of reasonable arch axis, the formation mechanism of arch effect of grout was also ascertained and verified by five numerical models. The results indicate that, when the radius of the haunch of CSP remained constant, the load-carrying capacity of the rehabilitated system increases upon decreasing of the radius of side walls and crown; when the side walls and crown of CSP remained constant, the load-carrying capacity of the rehabilitated system increases with the increase of the radius of the arch haunches; the most effective way to improve the load-carrying capacity of the rehabilitated system is to increase the radius of the haunches, followed by reducing the radius of the vault and side wall. The arch effect of grout was related to the load types. When the shape of CSP can keep the arch axis continuous and within the rigid angle of grout, the load-carrying capacity of the rehabilitated system is the highest. The structural design of the RC slab culvert rehabilitated with CSP should comprehensively consider the shapes of CSP and the arch effect of grout according to the actual needs of the project, and try to avoid the rehabilitation design close to the original RC slab culvert as far as possible.

With the continuous advancement and popularization of autonomous driving technology, more and more vehicles with autonomous driving technology will appear on the road, and the service objects of road markings will gradually transition from drivers to autonomous vehicles. On the one hand, the method of road markings condition assessment requires a lot of manpower to inspect, measure and evaluate; on the other hand, the evaluation index is based on biological vision research, which does not conform to the characteristics of automatic driving vehicles based on machine vision. To solve the above problems, this paper proposed a method of road markings condition assessment for autonomous vehicles. First, PSNR (peak signal-to-noise ratio) was initially determined as the evaluation index by means of literature review, analogical reasoning and logical reasoning. Secondly, to quickly obtain PSNR, this paper proposes a calculation method of the PSNR based on image inpainting, which utilizes the Deblur-GAN model restores the damaged road markings at the image level, and then uses the damaged and restored road markings images to calculate the PSNR. In addition, this paper proposed a data augmentation method that can realistically synthesize damaged road markings images to improve the performance of image inpainting models. Then,the AlexNet network was used as the benchmark model to design experiments to study the relationship between the PSNR and the recognition accuracy of road markings. The experimental results show that, compared with the calculation method of the PSNR based on the artificially restored image, the average PSNR obtained by the method proposed in this paper only differs by about 2.24%, but the acquisition speed is increased by about 418 times; when the average PSNR differs by about 43.66%, the average recognition accuracy differs by about 36.27%. Therefore,the PSNR can measure the use of road markings. Compared with the evaluation method of the current standard, the evaluation method proposed in this paper improves the work efficiency by about 6.5 times and consumes less manpower. And it is more in line with the characteristics of self-driving cars, but the evaluation methods are more detailed in the specification.

The construction of 3D road digital model is of great significance for intelligent vehicle service and road management. In this paper, to solve the problems such as fast running speed, interference noise, few features and no loopback detection assistance existing in different sections of highway application scenarios, a three-dimension highway modeling method with lidar information as the modeling data base is proposed, in which multi-sensor fusion based on lidar odometry and LOAM technology is adopted. In the investigation, firstly, the point cloud data in different road scenarios are obtained by lidar, and the lidar image segmentation technique is used to assign each point a label about the structure and exclude the information of other moving vehicles on the road to reduce the modeling noise. Then, an accurate synchronization strategy is developed to integrate the sensors such as GNSS, IMU and lidar. On this basis, by combining the inertial navigation pre-integration results, the position constraint based on feature point cloud and the RTK data, a three-dimension highway digital model with global consistency is constructed to eliminate the cumulative error of the lidar odometry. Moreover, in order to maintain a finite number of attitude estimates, a sliding window optimization strategy based on key frames is introduced. Finally, three common road sections (general, bridge and tunnel) in the highway scenario are collected for modeling analysis, and the results show that the proposed approach can effectively improve the robustness, accuracy and validity in the challenging highway scenario modeling.