Intelligent vehicle path tracking is challenged by uncertain disturbances, such as modeling inaccuracies and external environmental influences, which will significantly compromise both the path tracking accuracy and stability. To address this, this paper proposes a fixed-time prescribed-performance (FTPP) path tracking control method based on adaptive neural network disturbance estimation. Firstly, a radial basis function neural network with an online-updated adaptive law is developed for real-time estimation of uncertain disturbances, effectively compensating for their impact within the control model. Subsequently, a backstepping controller with FTPP is designed by integrating a composite dynamic surface control method with finite-time control techniques. This approach not only enhances the system convergence rate but also mitigates the derivative explosion problem inherent in traditional backstepping, yielding a control law with adaptive disturbances compensation for precise steering control. Finally, based on Lyapunov stability analysis, the boundedness of the closed-loop signals is established under the given assumptions, and the lateral path tracking error is shown to remain within the prescribed-performance bounds under feasible initial conditions. CarSim-Simulink-based co-simulation results validate the effectiveness of the proposed control method in improving both path tracking accuracy and stability.
Pingshu Ge, Chenyang Xu, Longxin Guan et al.· Engineering Research Express· 0 citations
Camera-based 3D object detection has attracted widespread attention for autonomous driving applications. However, existing methods often lack effective feature screening mechanisms, resulting in an extremely low spatio-temporal signal-to-noise ratio in complex scenes. Specifically, distracting background projections, feature misalignment caused by dynamic objects, and frequent occlusions jointly lead to severe ambiguity and loss of object features. To alleviate these issues, we propose STRDet, a robust 3D object detection framework based on progressive spatio-temporal feature refinement. First, we propose a Semantic-guided Context Refinement (SCR) module that explicitly suppresses background interference prior to the view transformation, thereby blocking noise propagation. Second, we design the Differential-aware Feature Alignment (DFA) and Residual-based Adaptive Gated Fusion (RAGF) modules, which leverage feature difference maps as motion saliency indicators to guide deformable alignment and employ gating mechanisms to selectively integrate historical motion cues, effectively resolving dynamic alignment failures and mitigating feature loss under occlusion. Extensive experiments on the nuScenes dataset demonstrate that STRDet effectively enhances feature purity and coherence, yielding significant improvements and achieving 46.95\% mAP and 55.37\% nuScenes detection score.
Bing Li, Lie Guo, Longxin Guan et al.· Measurement science and tech...· 0 citations