Research on Optimizing Control Signals for Single-Agent Navigation under Multiple Scenarios in Linear Motion

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已出版: 12月 17, 2025

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Chuanji Xu
Faculty of Engineering and Technology, Panyapiwat Institute of Management, Nonthaburi 11120, Thailand
Jian Qu
Faculty of Engineering and Technology, Panyapiwat Institute of Management, Nonthaburi 11120, Thailand

摘要

This study proposes a Bezier curve optimization method for enhancing the control signals and trajectory tracking performance of the Donkey Car, a 1:16 scale autonomous vehicle platform. The method employs second-order Bezier curves for throttle optimization and third-order Bezier curves for steering angle optimization. A novel loss function, Bezier Smoothing Loss (BSL), is introduced to simultaneously optimize control signal smoothness and trajectory tracking accuracy during neural network controller training.Experiments in three scenarios (left lane driving with obstacle avoidance, straight line driving, and straight driving with continuous obstacle avoidance) show that the proposed method significantly improves trajectory tracking accuracy (RMSE reduced by up to 19.6%), control signal smoothness (throttle change rate standard deviation decreased by 28.6%, steering angular velocity standard deviation reduced by 24.5%), and vehicle posture stability (yaw rate and pitch rate RMS values decreased by 7.9%). Compared to other learning-based methods (KerasLinear, KerasRNN, PBLM-CNN21, MFPE-CNN14), our approach achieves superior performance across all evaluation metrics.The proposed Bezier curve optimization approach effectively refines the performance of autonomous driving systems and offers a promising direction for future research and development in this domain.

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Chuanji Xu, & Qu, J. (2025). Research on Optimizing Control Signals for Single-Agent Navigation under Multiple Scenarios in Linear Motion. Science & Technology Asia, 30(4), 147–168. 取读于 从 https://ph02.tci-thaijo.org/index.php/SciTechAsia/article/view/254400
期
Vol.30 No.4 (October-December 2025)
栏目
Physical sciences
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