Abstract
Path smoothness is essential in path tracking for autonomous driving, while smooth means that the curvature of path is continuous. Parameterizing the path as a profile of \(curvature\ \kappa \), that is a function of \(arc\ length\ s\), not only simplifies description of the reference path, but also improve path tracking performance. To achieve this, there must be a trade-off between the smoothness of the curvature profile and the precision of the curve-fitting. This paper describes an algorithm which fits the reference path described by a sequence of cartesian points. The algorithm utilizes piecewise linearized \(\kappa -s\) profile to represent the reference path. The curvature profile is smooth subject to the constraints of lateral deviation.
The research was partly funded by the Natural Science Foundation of China under Grant 62003362.
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© 2023 Beijing HIWING Sci. and Tech. Info Inst
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Zhong, W., Li, X., Sun, Z. (2023). Generating Parameterized Smooth Paths with a Curve-Fitting Algorithm. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_220
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