CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Yunxiao Shan,Cheng Chen,Ling Zheng,Wei Yang,Jian Zhou,Bijun Li

doi:10.5772/61391

Abstract

Simple and efficient geometric controllers, like Pure-Pursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CF-Pursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path's curvature. There are three input variables in this fuzzy system, 6 mcurvature, 9 mcurvature and 12 mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.

Highlights

An autonomous vehicle can drive by itself with necessary sensors, such as GPS, IMU, cameras, and Lidar
We need to consider the dynamic effects such as side slip when we drive at high speeds, which is difficult for the geometric controllers to apply currently
This paper tries to solve for urban applications, requiring the autonomous vehicle to perform common urban maneuvers like turning left or right, turning around and changing lanes

Summary

Introduction

An autonomous vehicle can drive by itself with necessary sensors, such as GPS, IMU, cameras, and Lidar. The process is that the vehicle first detects the environment and positions itself according to these sensors, and navigates with global and local planner; the vehicle drives by sending the control commands from the pathtracking controller to the executing mechanisms. With consideration to the process described above, we can see that the path-tracking controller is a bridge that connects the upper software and the lower hardware. Snider [1]: Path tracking refers to a vehicle executing a globally defined geometric path by applying appropriate steering motions that guide the vehicle along that path. A good path-tracking controller minimizes the lateral distance and heading between the vehicle and the defined path.

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Conclusion