Research and Application of Improved Pure Pursuit Algorithm in Low-Speed Driverless Vehicle System
Xiaowei Chen, Po Li, Qingyu Zhang, Zhigang Jin · 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · 2022
Low-speed driverless vehicle with automatic driving as the operating system came into being with the development of vehicle networking technology and the construction of new infrastructure such as 5G base stations. However, low-speed driverless vehicle does not have to face the complex high-speed environment, compared with driverless passenger vehicles. Especially, driverless vehicle accurately tracks the path to ensure the safe and stable arrival of vehicles at their destination. In this paper, a improved pure pursuit path tracking algorithm based on reference path curvature adaptive adjustment of looking-forward distance is proposed. Using this method, the non single looking-forward distance is adopted on the whole reference path to ensure that the looking-forward distance is changed according to the curvature change of the reference path within the preview distance during driving, so as to ensure the tracking effect of vehicles in curves, turns and other sections, reduce cutting-corner and overshoot problems. With many tests, the improved pure pursuit path tracking algorithm which adaptively adjusts the looking-forward distance according to the path curvature can ensure the good path tracking performance of unmanned vehicles in curves and so on.