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Hybrid Reinforcement Learning-Based Collision Avoidance Algorithm for Autonomous Vehicle Clusters

IEEE ACCESS 2025年 13卷 61564-61577页

作者： Guo, Chubing Wu, Jianshe Luo, Panzheng Wang, Zhigang Zhang, Kai Yang, Ziyi Dou, Zengfa Song, Kan Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China 20th Res Inst China Elect Technol Grp Corp Natl Key Lab Multidomain Data Collaborat Proc & Co Xian 710068 Shaanxi Peoples R China Northwestern Polytech Univ Sch Software Xian 710072 Shaanxi Peoples R China Xian Aeronaut Comp Tech Res Inst AVIC Xian 710065 Peoples R China Xian Univ Sch Informat Engn Xian 710065 Shaanxi Peoples R China Postal Savings Bank China Beijing 100068 Peoples R China

Nowadays, collaborative collision avoidance for autonomous vehicle clusters has become the key to ensure traffic safety. Aiming at the complex and changeable traffic environment, this paper proposes a novel collision avoidance method for Autonomous Vehicle Clusters based on hybrid reinforcement learning. The method combines the adaptive capability of reinforcement learning with the feature extraction capability of deep learning to improve the collision avoidance performance in complex traffic scenarios. A hybrid reinforcement learning framework is designed, which consists of a deep neural network structure and a reinforcement learning structure. The feature extraction key from environmental perception data and predict possible collision risks. While the latter learns how to adjust vehicle motion parameters based on these features and the historical performance of collision avoidance strategies. Convolutional neural network is used to process image data to capture spatial information in traffic scenes, and time series data is combined with long and short time memory network to capture the time dependence of vehicle motion. Through a large number of simulation experiments and field tests from KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute), nuScenes and INTERACTION, we verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm in this study showed a high success rate of collision avoidance and a low average reaction time under most collision avoidance times.

关键词： Collision avoidance Autonomous vehicles Reinforcement learning Feature extraction Deep learning clustering algorithms Decision making Accuracy Prediction algorithms Computational modeling vehicular engineering autonomous vehicle control collision avoidance

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A Low-Power clustering Routing Protocol Optimization Scheme for Substation WAPI 5

A Low-Power Clustering Routing Protocol Optimization Scheme ...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Xing, Yang Linhui, Yang State Grid Qinghai Electric Power Company Electric Power Dispatching Control Center Qinghai Xining China

ISBN: (纸本)9798331533694

This study centers on the substation application scenario and offers an optimized solution for the low-power LEACH protocol used in substation WAPI. The algorithm put forward herein comprehensively considers the current remaining energy of nodes as well as the node distribution location. By bringing in the remaining energy factor and the distance factor, it optimizes the formula for the cluster-head election threshold. This optimization makes the cluster-head election more scientific and the energy consumption distribution more uniform. Furthermore, the simulation results show that compared with the traditional LEACH algorithm, the EHN-LEACH algorithm proposed in this paper can further reduce the network energy consumption and prolong the network usage time. © 2025 IEEE.

关键词： clustering algorithms

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MTOClus: Multi-Type Objects clustering in Heterogeneous Information Networks 3

MTOClus: Multi-Type Objects Clustering in Heterogeneous Info...

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3rd International Conference on Communications, Information System, and Data Science, CISDS 2024

作者： Liang, Yongjie Hu, Wujie Wu, Junjie Wu, Jinzhao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Electric Engineering Guangxi University Nanning China Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

clustering is crucial for analyzing heterogeneous information networks (HINs). Mainly state-of-the-art algorithms often focus on single-type node clustering, overlooking the clustering of multiple node types and requiring manual setting of cutoff distances and clustering centers. This paper introduces a clustering algorithm integrating multi-type objects, named MTOClus, which generates multiple similarity matrices for different node types and assigns weights to each matrix to indicate their significance. By aggregating weighted similarity matrices, it creates a distance matrix. MTOClus automates cluster center selection through node sorting and mitigates the impact of outliers by utilizing the K-Nearest Neighbors approach to compute the cutoff distance. Experimental results across four datasets consistently show that MTOClus outperforms six other algorithms, underscoring its superior clustering efficacy. © 2025 SPIE.

关键词： clustering algorithms

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Object Detection via Active Learning Strategy Based on Saliency of Local Features and Posterior Probability

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IEEE ACCESS 2024年 12卷 35462-35474页

作者： Wang, Meng Liu, Guobao Liu, Haipeng Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming Peoples R China

Active learning is a strategy that involves the deliberate selection of training samples with significant discriminative features, to formulate a more efficient learning paradigm. While active learning has seen success in image classification, its application in object detection remains complex due to the multiple candidate object instances in an image and the interference of background regions on these instances. To tackle the challenge of sample selection in active learning for large object recognition datasets, this paper introduces a active learning method based on local feature saliency and posterior probability. Initially, the method selects active learning samples with discriminative saliency by clustering the multi-scale feature maps of the processed samples by self-attention weights. Subsequently, it calculates the posterior entropy based on the posterior probability of the current model's candidate instances. This is combined with the entropy encoding of feature points corresponding to instances in the cluster to further select uncertain samples in the object set. On the COCO dataset, our method demonstrates superior overall performance in the learning curve compared to existing object detection baselines. Moreover, under the same training configuration, the final detection accuracy shows a notable improvement of at least 2.36%, thus confirming the effectiveness of our method in enhancing learning and detection performance.

关键词： Feature extraction Uncertainty Object detection Training data Task analysis Detectors Data models Unsupervised learning clustering algorithms active learning unsupervised learning feature cluster self attention

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Reinforcement Learning Placement Algorithm for Optimization of UAV Network in Wireless Communication

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IEEE ACCESS 2024年 12卷 37919-37936页

作者： Baghdady, Sahar Mirrezaei, Seyed Masoud Mirzavand, Rashid Shahrood Univ Technol Elect Engn Dept Shahrood 36199-95161 Iran Univ Alberta Elect & Comp Engn Dept Edmonton AB T6G 1H9 Canada

Unmanned aerial vehicles (UAVs) or drones have attracted much attention in wireless communication networks because of their agility, unique flexibility, low cost of implementation, and the high strength of the line-of-sight (LoS) channel. They are widely used in different scenarios. In many environments with complex geographical conditions or in situations where areas are affected by natural disasters, UAVs can be used as base stations (BSs) for downlink ground users. The article proposes a communication system using multiple UAV-mounted BSs to improve coverage rate and minimize the number of required UAVs. The problem is formulated as a mixed-integer programming problem with constraints on the quality of service (QoS) and serviceability of each UAV. A three-step method is developed to solve the problem, which includes deriving the maximum service radius of UAVs using the Karush-Kuhn-Tucker (KKT) method, minimizing the number of required UAVs using reinforcement learning (RL) algorithm, and designing the three-dimensional (3D) position and frequency band of each UAV to increase signal power and reduce interference. The simulation results show that the RLP algorithm outperforms other algorithms in terms of coverage rate, user clustering, increased signal, reduced interference, and processing time required to find the optimal solution.

关键词： Drones Three-dimensional displays Interference Autonomous aerial vehicles Signal to noise ratio Quality of service clustering algorithms Wireless communication Agile software development Communication networks Optimal control Interference channels unmanned aerial vehicles (UAVs) base stations (BSs) three-dimensional (3D) deployment user clustering

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Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4827-4842页

作者： Xu, Wenkang Liu, An Zhao, Min-jian Caire, Giuseppe Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Tech Univ Berlin Dept Telecommun Syst D-10587 Berlin Germany

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where near-field propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e., it has a certain VR over the antennas. Because of the spatial correlation among adjacent sub-arrays, VR of scatterers exhibits a two-dimensional (2D) clustered sparsity. We design a 2D Markov prior model to capture such a structured sparsity. Based on this, a novel alternating maximum a posteriori (MAP) framework is developed for high-accuracy VR detection and channel estimation. The alternating MAP framework consists of three basic modules: a channel estimation module, a VR detection module, and a grid update module. Specifically, the first module is a low-complexity inverse-free variational Bayesian inference (IF-VBI) algorithm that avoids the matrix inverse via minimizing a relaxed Kullback-Leibler (KL) divergence. The second module is a structured expectation propagation (EP) algorithm which has the ability to deal with complicated prior information. And the third module refines polar-domain grid parameters via gradient ascent. Simulations demonstrate the superiority of the proposed algorithm in both VR detection and channel estimation.

关键词： Channel estimation Antennas Vectors clustering algorithms Signal processing algorithms Matching pursuit algorithms Millimeter wave communication Massive MIMO Inference algorithms Complexity theory VR detection channel estimation XL-MIMO 2D clustered sparsity alternating MAP IF-VBI structured EP

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Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第6期23卷 6124-6138页

作者： Zaher, Mahmoud Bjornson, Emil Petrova, Marina KTH Royal Inst Technol Div Commun Syst S-16440 Stockholm Sweden KTH Royal Inst Technol Div Commun Syst S-16440 Stockholm Sweden Rhein Westfal TH Aachen Mobile Commun & Comp Grp D-52062 Aachen Germany

This paper considers a mmWave cell-free massive MIMO (multiple-input multiple-output) network composed of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. We address UE mobility in the downlink (DL) with imperfect channel state information (CSI) and pilot training. Aiming at extending traditional handover concepts to the challenging AP-UE association strategies of cell-free networks, distributed algorithms for joint pilot assignment and cluster formation are proposed in a dynamic environment considering UE mobility. The algorithms provide a systematic procedure for initial access and update of the serving APs and assigned pilot sequence to each UE. The principal goal is to limit the necessary number of AP and pilot changes, while limiting computational complexity. We evaluate the performance, in terms of spectral efficiency (SE), with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithms effectively identify the essential AP-UE association refinements with orders-of-magnitude lower computational time compared to the state-of-the-art. It also provides a significantly lower average number of pilot changes compared to an ultra-dense network (UDN). Moreover, we develop an improved pilot assignment procedure that facilitates massive access to the network in highly loaded scenarios.

关键词： Millimeter wave communication Massive MIMO Handover clustering algorithms Heuristic algorithms Wireless communication Distributed algorithms Cell-free massive MIMO handover cluster formation pilot assignment mobility management spectral efficiency

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Defective Pixel Corrector for Line Scan and Area Scan Image Sensors

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Peng, Liyuan Huang, Yujie Wang, Mingyu Li, Wenhong Jing, Minge Zeng, Xiaoyang Fudan Univ Sch Microelect State Key Lab Integrated Chips & Syst Shanghai 200000 Peoples R China Shanghai ExploreX Technol Co Ltd Shanghai 200120 Peoples R China

The defective pixel corrector is an essential component of the image processor, which detects and corrects defective pixels in the image. Processing discrete defective pixels that exist in the output of area-scan image sensors is the focus of current research. However, the case of defective columns produced by line scan image sensors is a massive challenge for existing algorithms. In this paper, we develop novel algorithms for the detection and correction of columnar defective pixels by modeling the properties of defective columns in the output images of line scan image sensors. In addition, we design the non-extremum verdict and texture adaptive correction for clustered defective pixels in the area scan sensors and apply them to current algorithms to get enhancement, and new algorithms are obtained. Moreover, we propose a generalized hardware architecture for detection and correction. In the experimental stage, the proposed methods are compared with the state-of-the-art methods, both at the algorithmic level and in hardware implementation. The experimental results show that, compared to the current widely used algorithms, our methods for line-scan image sensors improve the detection rate by 28%, the detection precision by 90%, and the image restoration quality by 35% with a 10% increase in hardware consumption. For area-scan image sensors, the proposed non-extremum verdict improves the detection rate by 25%, improves the precision from 0.056 to 0.983, and the texture adaptive correction mitigates the image blurring caused by the correction process, with a 37% improvement in image quality, while incurring only a 1% increase in hardware overhead. In addition, our proposed algorithms achieve comparable detection and correction results with far fewer computational and storage resource requirements than machine learning-based algorithms.

关键词： Image sensors Hardware clustering algorithms Heuristic algorithms Imaging Computer architecture Photoreceptors Machine learning algorithms Training Image restoration Defective pixel detection and correction area scan image sensors line scan image sensors non-extremum verdict texture adaptive correction

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clustering Analysis via Deep Generative Models With Mixture Models

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第1期33卷 340-350页

作者： Yang, Lin Fan, Wentao Bouguila, Nizar Huaqiao Univ Dept Comp Sci Xiamen 361021 Peoples R China Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ H3G 1T7 Canada

clustering is a fundamental problem that frequently arises in many fields, such as pattern recognition, data mining, and machine learning. Although various clustering algorithms have been developed in the past, traditional clustering algorithms with shallow structures cannot excavate the interdependence of complex data features in latent space. Recently, deep generative models, such as autoencoder (AE), variational AE (VAE), and generative adversarial network (GAN), have achieved remarkable success in many unsupervised applications thanks to their capabilities for learning promising latent representations from original data. In this work, first we propose a novel clustering approach based on both Wasserstein GAN with gradient penalty (WGAN-GP) and VAE with a Gaussian mixture prior. By combining the WGAN-GP with VAE, the generator of WGAN-GP is formulated by drawing samples from the probabilistic decoder of VAE. Moreover, to provide more robust clustering and generation performance when outliers are encountered in data, a variant of the proposed deep generative model is developed based on a Student's-t mixture prior. The effectiveness of our deep generative models is validated though experiments on both clustering analysis and samples generation. Through the comparison with other state-of-art clustering approaches based on deep generative models, the proposed approach can provide more stable training of the model, improve the accuracy of clustering, and generate realistic samples.

关键词： Generative adversarial networks Gallium nitride Generators Training clustering algorithms Data models Decoding clustering generative adversarial network (GAN) mixture models student's-t mixture model variational autoencoder (AE) variational inference Wasserstein GAN

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Cluster Guided Truncated Hashing for Enhanced Approximate Nearest Neighbor Search

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 181-185页

作者： Liu, Mingyang Yang, Zuyuan Han, Wei Xie, Shengli Guangdong Univ Technol Sch Automat Guangdong Prov Key Lab Intelligent Syst & Optimiza Guangzhou 510006 Peoples R China Guangdong Hong Kong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China Guangzhou Railway Polytech Guangzhou 510006 Peoples R China Sch Automat Key Lab Intelligent Informat Proc & Syst Integrat Minist Educ Guangzhou 510006 Peoples R China Minist Educ Key Lab iDetect & Mfg IoT Guangzhou 510006 Peoples R China

Hashing is essential for approximate nearest neighbor search by mapping high-dimensional data to compact binary codes. The balance between similarity preservation and code diversity is a key challenge. Existing projection-based methods often struggle with fitting binary codes to continuous space due to space heterogeneity. To address this, we propose a novel Cluster Guided Truncated Hashing (CGTH) method that uses latent cluster information to guide the binary learning process. By leveraging data clusters as anchor points and applying a truncated coding strategy, our method effectively maintains local similarity and code diversity. Experiments on benchmark datasets demonstrate that CGTH outperforms existing methods, achieving superior search performance.

关键词： Encoding Quantization (signal) Binary codes clustering algorithms Vectors Indexes Search problems Principal component analysis Nearest neighbor methods Information technology Anchor hashing projection quantization

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