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CSME 2023/05
Volume 44 No.2
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165-178
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Optimization and Design of Driverless Vehicle Software System Based on Image Recognition
Bi Lia, Bing-Rong Zhangb, Yi-Jui Chiub, Cai-En Wengc, Yuh-Chung Hud, Ji-Ming Yib and Sheng-Rui Jiane
aSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China.
bSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian Province, China.
cSchool of Mechanical Engineering, Sanming University, Sanming, Fujian, China.
dDepartment of Mechanical and Electro-Mechanical Engineering, National Ilan University, I-lan Taiwan.
eDepartment of Materials Science and Engineering, I-Shou University, Kaohsiung 840, Taiwan.
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Abstract:
An intelligent vehicle is a system that integrates environment perception, path decision planning, auto-matic driving, and other functions. In order to improve the tracking and motion performance of intelligent vehicles, a system that includes image preprocessing, image processing, path tracking planning, and intelli-gent vehicle control is designed and optimized. First, the principles and implementation effects of three basic threshold algorithms and the image denoising algorithm are discussed. Second, the traditional edge extraction algorithm and track condition judgment al-gorithm are improved. Then, a path tracking planning method based on the midline algorithm and an edge fitting algorithm based on the least square algorithm are proposed and simplified. Finally, aiming at solving the shortcomings of the traditional PID algorithm that cannot update the values of Kp, Ki and Kd, an intelligent vehicle control system based on the PID algorithm and fuzzy control is proposed and verified by simulation and experiment. The results show that the designed filtering algorithm can effectively reduce the image noise. The improved edge extraction algorithm has an obvious filtering effect on the abnormal data in the process of intelligent vehicle operation. The difference between the straight and bent track obtained by the improved track condition judgment algorithm is 7.39, which is larger than 1.78 obtained by the traditional algorithm. The improved algorithm is sensitive to the change in the track bending degree and overcomes the problem that the performance of the traditional algo-rithm decreases with the bending degree. Using the simplified edge algorithm, an edge fitting algorithm based on the least square algorithm is developed. This algorithm is similar to the algorithm with the R-squared greater than 0.994, and the number of edge points used for calculation is reduced from the original 48 points to 2 or 3 points, which greatly improves the operation efficiency of the intelligent vehicle. Using the fuzzy-based PID control algorithm, after the target speed changes, the output curve reaches the target speed at 0.44 s, the maximum excess is approximately 16.4 rpm, and the algorithm becomes stable at the target speed after 7.9 s, which is less than 1.2 s, 56.7 rpm and 12.1 s of traditional PID control respectively. Thus, using the proposed fuzzy-based PID control algorithm, the control performance of the system can be significantly improved. The experimental results of real intelligent vehicle show that the proposed fuzzy PID control algorithm can significantly improve the control effect under high-speed operation.
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Keywords: Intelligent vehicle, Image preprocessing, Edge extraction algorithm, Track condition judgment algorithm, Path tracking planning, Fuzzy-based PID control.
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*Corresponding author; e-mail: ychu@niu.edu.tw
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©
2023
CSME , ISSN 0257-9731
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