Adaptive coordination control strategy for path-following of DDAEV with neural network based moving weight coefficients

Xie, Ju; Xu, Xing; Wang, Feng; Tang, Zhao; Chen, Long

doi:10.1177/01423312221135255

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Dewangan and Sahu 3 proposed a new lane detection framework based on a two-layer deep learning approach for multiple images under different weather conditions. Xie et al 4 developed a model predictive control algorithm that enables adaptable and dependable vehicle control through autonomous steering. Wang et al 5 proposed a method for model predictive control based on variable prediction horizons, which enhances vehicle path tracking performance.…”

Section: Introductionmentioning

confidence: 99%

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement

Guo,

Wang,

Zhang

et al. 2024

Science Progress

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The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

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Section: Introductionmentioning

confidence: 99%

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement

Guo,

Wang,

Zhang

et al. 2024

Science Progress

View full text Add to dashboard Cite

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Fault detection and fault-tolerant control of autonomous steering system for intelligent vehicles combining Bi-LSTM and SPRT

Xie

et al. 2023

Measurement

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Research on AUV dynamic obstacle avoidance planning based on rolling speed obstacle method

Zhang,

Wang,

Tang

et al. 2024

Transactions of the Institute of Measurement and Control

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In this paper, aiming at the problem of poor path planning and obstacle avoidance effect of autonomous underwater vehicle (AUV) in a dynamic environment, a feasible rolling speed obstacle method is proposed. This method combines the rolling window method with the speed obstacle method, and designs a suitable three-dimensional model predictive controller based on the rolling window method under a hybrid obstacle avoidance structure, and achieves stable tracking of the reference path by optimizing the objective function. A three-dimensional collision cone and speed obstacle cone model is constructed while the window is rolling. If the collision avoidance condition is met, the critical collision point is calculated, and the AUV is guided to avoid obstacles safely by tracking the critical collision point; if collision avoidance ends, guide AUV trajectory recovery. The final simulation and experimental results show that the performance of the rolling speed obstacle method in avoiding dynamic obstacles is 30% higher than the rolling window method and 40% higher than the speed obstacle method. The method used in this paper can effectively improve the dynamic obstacle avoidance ability of AUV in real-time path planning.

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Adaptive coordination control strategy for path-following of DDAEV with neural network based moving weight coefficients

Cited by 4 publications

References 36 publications

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement

Fault detection and fault-tolerant control of autonomous steering system for intelligent vehicles combining Bi-LSTM and SPRT

Research on AUV dynamic obstacle avoidance planning based on rolling speed obstacle method

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