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IEEE Transactions on intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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Local Map Construction Based on 3D-LiDAR and Camera

Local Map Construction Based on 3D-LiDAR and Camera

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Chinese control Conference (CCC)

作者： Hui Qin Jing Li Junzheng Wang Qingbin Wu Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The local map can update the local environment information in real time, which provides the environment information for the local dynamic planning of the robot. In this paper, a local cost map construction method based on 3D-LIDAR and camera is proposed. We use camera to detect lane lines in structured road and 3D-LIDAR to detect road boundaries in unstructured environment, and then use DS evidence reasoning to determine the current local road information. Dynamic obstacle information in the environment is obtained through 3D point cloud data segmentation, which is fused with the road information to get 3D point cloud information in the local range and generate local cost map according to it. Experiments show that the method in this paper can accurately extract the current road information whether in structured roads or unstructured roads. The fused local cost map can enable the robot to perform reasonable local planning and complete navigation on the current road.

关键词： Roads Three-dimensional displays Robots Planning Cameras Real-time systems Navigation

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ICP algorithm point cloud registration method based on a new improved Harris Hawk algorithm

ICP algorithm point cloud registration method based on a new...

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Chinese Automation Congress (CAC)

作者： Chen Wang Shangbin Jiao Rui Gao Yuxing Li School of Automation and Information Engineering Xi’an University of Technology Xi’an China School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China School of Electronic & Electrical Engineering Baoji University of Arts and Sciences Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology China

ISBN: (纸本)9781665426480

To address shortcomings of ICP algorithm, which is sensitive to the initial position and easy to fall into local optimum, we propose a Harris Hawks Optimizer (HHO) point cloud registration method combining PCA preprocessing and parallel population initialization. The PCA method is used to align the point clouds with the main axes and perform the main axes correction. Since the six input variables in the population initialization stage of HHO point cloud registration are different and independent, the initial population of HHO algorithm is initialized by the parallel initialization method. The results show that the improved algorithm is able to successfully overwhelm the fault of ICP algorithm, and its global seek performance and optimization accuracy are more advantageous, and the algorithm operates efficiently, with high registration accuracy and robustness.

关键词： Point cloud compression Automation Input variables Sociology Robustness Statistics Optimization

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Design and Implementation of Number Gesture Recognition system Based on Kinect

Design and Implementation of Number Gesture Recognition Syst...

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第三十九届中国控制会议

作者： Xingxiu He Jia Zhang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control of Complex System

The paper designs a number gesture recognition system based on Kinect. The system uses the depth image captured by Kinect sensor to extract gesture information for recognition. In the process of recognition, the AdaBoost algorithm in machine learning is adopted firstly, by the help of Visual Gesture Builder to train the number gesture classifier,and export the classifiers to the recognition program to recognize the current gesture. Finally, numbers are outputted according to the recognized gesture, and the basic functions of number gesture recognition system are realized. Through the experimental verification, the recognition system has enough accuracy, and the system is suitable for various factors such as environment, personnel, etc., which has a certain practical value.

关键词： Kinect sensor number gesture recognition Adaboost algorithm human-computer interaction

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A Real-time Fire Segmentation Method Based on A Deep Learning Approach

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IFAC-PapersOnLine 2022年第6期55卷 145-150页

作者： Mengna Li Youmin Zhang Lingxia Mu Jing Xin Ziquan Yu Shangbin Jiao Han Liu Guo Xie Yi Yingmin Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an Shaanxi 710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec H3G 1M8 Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics (NUAA) Nanjing Jiangsu China

As a kind of the forest “fault”, fire is highly destructive and difficult to rescue. Fire segmentation is helpful for firefighters to understand the fire scale and formulate a reasonable fire-fighting plan. Therefore, this paper proposes a real-time fire segmentation method based on deep learning. This method is an improved version of deeplbav3+, which is an encoder-decoder structure network. Encoder network is composed of deep convolutional neural network and atrous spatial pyramid pooling. Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy. Therefore, in order to compensate for the loss of segmentation accuracy, on the basis of the original decoder network, this paper adds two different shallow features to make the network contain rich fire feature information. Experimental results show that the comprehensive performance of this method is better than the original deeplabv3+, especially the segmentation speed of the network is greatly improved, which is about 59 FPS.

关键词： Forest fire Fire segmentation Atrous spatial pyramid pooling Mobilenetv3 Deeplabv3+

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A non-uniform Gauss pseudospectral method based on curvature information for dynamic optimization problem

A non-uniform Gauss pseudospectral method based on curvature...

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第32届中国过程控制会议(CPCC2021)

作者： Lu Lv Qian Zhang Jianzhong Su Wenhai Wang Jianquan Yuan Zijian Tian Zhenghui Zhang Long Xiao Zeyin Zhang Xinggao Liu Zhejiang University NGICS Platform State Key Laboratory of Industrial Control TechnologyControl DepartmentZhejiang University Tianjin Jinhang Institute of Technical Physics Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Mathematics Department Zhejiang University

Dynamic optimization for hypersonic reentry vehicle(HRV) is a challenge for complex nonlinear constraints during a global strike *** to the strong nonlinearity of HRV,it is difficult to guarantee the precision of complex constraints on non-collocation points with traditional Gauss pseudospectral method(GPM).A novel approach which provides an optimal control strategy considering the above challenges is then ***,an adaptive mesh refinement strategy based on curvature information is presented to improve the accuracy of GPM.A collocation point redistribution method based on error analysis is then proposed to further improve the accuracy of the ***,a detection mechanism is proposed to correct the results on-line when the path constraint is not *** proposed approaches are finally applied to a classic HRV problem and compared with other literature work in *** research results validate the effectiveness of the proposed methods.

关键词： nonlinear optimal control Gauss pseudospectral method adaptive mesh refinement hypersonic reentry vehicle

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Early Forest Fire Segmentation Based on Deep Learning

Early Forest Fire Segmentation Based on Deep Learning

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Li, Mengna Zhang, Youmin Xin, Jing Mu, Lingxia Yu, Ziquan Liu, Han Xie, Guo Jiao, Shangbin Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China

ISBN: (纸本)9781665401159

Fire segmentation is very important for fire rescue. It can make firefighters get the information on fire area, spread direction and so on, and then help them make quick and effective fire-fighting plan. Therefore, this paper proposes an early forest fire segmentation algorithm based on a deep learning model, named F-Unet, which mainly uses the architecture idea of Unet for reference. F-Unet consists of contraction path, feature fusion layer and expansion path. The contraction path is composed of the first 13 layers of VGG16, which is used to obtain feature maps with different scales. In order to improve the segmentation accuracy of the model, the feature fusion network proposed in this paper is added to the Unet architecture to fuse these feature maps with different scales. The expansion path is used for upsampling these feature maps to restore the size of the original input image and obtain the fire segmentation results. The experimental testing results on the FLAME dataset show that F-Unet can significantly improve the fire segmentation precision, and also prove that the proposed feature fusion network is effective to improve the performance of fire segmentation. © 2021 IEEE.

关键词： Deforestation

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Research on Weighted Model of Air Defense Weapon-Target Assignment Based on AHP and Entropy Method

Research on Weighted Model of Air Defense Weapon-Target Assi...

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Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), International Conference on

作者： Puyang Zhang School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System Beijing P. R. China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

In many previous studies, the analytical model of weapon-target assignment (WTA) is only based on the maximum damage to hostile targets. In order to overcome the deficiency of single decision-making, three sub models are established, which not only take the maximum damage to hostile targets, but also minimum loss and shortest interception time as the optimization objectives. In the meantime, the established models combine the air defense operation pattern and the characteristics of multiple air defense platforms. Based on the sub models, a weighted WTA model is proposed in this study using analytic hierarchy process (AHP) and entropy method. Through optimization, the optimal air defense WTA scheme is obtained by the weighted WTA model, and the optimization results are analyzed. Comparative study demonstrates that it is more reasonable to optimize the WTA scheme with multi-objectives.

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Distributed Event-Triggered Algorithm for Convex Optimization with Coupled Constraints

arXiv

引用

arXiv 2022年

作者： Huang, Yi Zeng, Xianlin Meng, Ziyang Sun, Jian School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Precision Instrument Tsinghua University Beijing100084 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

This paper develops a distributed primal-dual algorithm via event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an O(1/k) convergence rate for general convex objective functions, where k > 0 is the iteration number. Moreover, by applying event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life prediction

arXiv

引用

arXiv 2021年

作者： Ayodeji, Abiodun Wang, Wenhai Su, Jianzhong Yuan, Jianquan Liu, Xinggao State Key Laboratory of Industrial Control Technology Control Department Zhejiang University Hangzhou310027 China Tianjin Jinhang Technical Physics Institute Tianjin300192 China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100074 China

A single unit (head) is the conventional input feature extractor in deep learning architectures trained on multivariate time series signals. The importance of the fixed-dimensional vector representation generated by the single-head network has been demonstrated for industrial machinery condition monitoring and predictive maintenance. However, processing heterogeneous sensor signals with a single-head may result in a model that cannot explicitly account for the diversity in time-varying multivariate inputs. This work extends the conventional single-head deep learning models to a more robust form by developing context-specific heads to independently capture the inherent pattern of each sensor reading in multivariate time series signals. Using the turbofan aircraft engine benchmark dataset (CMAPSS), an extensive experiment is performed to verify the effectiveness and benefits of multi-head fully connected neurons, recurrent networks, convolution network, the transformer-style stand-alone attention network, and their variants for remaining useful life estimation. Moreover, the effect of different attention mechanisms on the multi-head models is also evaluated. In addition, each architecture's relative advantage and computational overhead are analyzed. Results show that utilizing the attention layer is task-sensitive and model dependent, as it does not provide consistent improvement across the models investigated. The result is further compared with five state-of-the-art models, and the comparison shows that a relatively simple multi-head architecture performs better than the state-of-the-art models. The results presented in this study demonstrate the importance of multi-head models and attention mechanisms to improved understanding of the remaining useful life of industrial assets. © 2021, CC BY.

关键词： Recurrent neural networks

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