Study on the Rut Control Threshold of Asphalt Pavement Considering Steering Stability of Autonomous Vehicles Based on Fuzzy Control Theory

Zheng, Binshuang; Huang, Xiaoming; Zhao, Renda; Hong, Zhengqiang; Chen, Jiaying; Zhu, Shengze

doi:10.1155/2021/8879900

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…In their study, Ferenčík et al 26 utilized Close-range photogrammetry to quantify the depth of the rut formed by wheeled skidders within the University Forest Enterprise of the Technical University located in central Slovakia. The study by Zheng et al 30 examined the impact of rut in asphalt surfaces on the steering stability of autonomous vehicles. The study's results indicate that the critical determinant of the vehicle’s maximum roll angle is the depth of the rut.…”

Section: Introductionmentioning

confidence: 99%

Identification of wheel track in the wheat field

Zhang

2024

Sci Rep

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Agriculture machinery navigating along permanent traffic lanes in the farmland may avoid causing extensive soil compaction. However, the permanent traffic lanes are frequently covered up or eliminated by following tillage practices. It is necessary to identify the wheel tracks designed as permanent traffic lanes in order to ensure the agriculture machinery travels along the designated wheel tracks when cultivating the field. This study proposed an identification method of wheel tracks based on the morphological characteristics of wheel tracks and the environmental conditions around the wheel tracks in the wheat fields. The proposed method first utilized the maximum interclass variance to identify the contours of the main part of the wheel track and the shadow regions around the wheel track’s edges. The main part of the wheel tracks was then separated from interference pixels by moving the centerline of the main part of the wheel track, which was derived by skeleton algorithm and curve fitting, towards the right or left edge of the wheel track at a specific distance. In a morphological opening operation, specific linear and circular structural elements were used to segment the shadow regions along the edge of the wheel track. The remaining wheel track was finally recognized by computing the complement of the region identified. After achieving the segmentation of wheel tracks, many reference points near the outside of the wheel track edge in the original image were chosen as fiducial points for evaluating the differences between the actual value and the recognized wheel track edge. The evaluation was based on computing the root mean squared error (RMSE) and the mean absolute error (MAE) of coordinates of reference points and recognized wheel track edge. The results showed that the largest RMSE and MAE were 24.01 pixels (0.0045 m) and 17.32 pixels (0.0032 m), respectively. The low values of RMSE and MAE reveal that the accuracy of the algorithm developed in this study is high, and using this algorithm may segment the wheel track in the wheat field accurately.

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Section: Introductionmentioning

confidence: 99%