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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 778-789页

作者： Dodge, Daniel Yilmaz, Muhittin Texas A&M Univ Kingsville Kingsville TX 78363 USA Texas A&M Univ Kingsville Elect Engn & Comp Sci Dept Kingsville TX 78363 USA

This study investigates the convex optimization frameworks for accurate and fast negative obstacle detection as well as depth estimations that are vital for real-time, superior autonomous vehicle operations. A convex framework is proposed in conjunction with a unique stereo perception approach to manage the complex nature of such invisible obstacles. The proposed stereo configuration incorporates an alignment that displaces the cameras along a vertical baseline to extract useful information pertaining to negative obstacle features for various illustrative terrain settings. The convex framework exploits these properties to evaluate depth jumps in the disparity space image and perform geometrical analysis of potential occlusion regions. The corresponding convex formulations are based on linear matrix inequalities to detect sudden disparity, angle profile, and intensity variations for potential negative obstacles and to estimate the internal depth of the detected obstacles. Experimental results demonstrate that consecutive processes can be structured in a convex framework to efficiently identify negative obstacle attributes at a range of distances for texture varying environments, providing a vital extension to real-time vehicular system implementations with superior detection rates.

关键词： Cameras Image color analysis Stereo vision Feature extraction detection algorithms Real-time systems Laser radar Negative obstacle convex optimization stereo vision autonomous ground vehicle

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A Pseudorange-Based GPS Spoofing detection Using Hyperbola Equations

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第8期72卷 10770-10783页

作者： Demir, Mehmet Ozgun Kurt, Gunes Karabulut Pusane, Ali Emre Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkiye Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada

Spoofing attacks are one of the most critical threats against secure global navigation satellite system (GNSS) positioning. Since correct positioning is a must in many vehicle-to-everything (V2X) communication systems, the detection of these attacks is vital. In the literature on the detection strategies of spoofing attacks, the majority of the solutions are based on the assumption that all available signals are spoofed. In this paper, we focus on detecting spoofing attacks in which authentic and spoofing positioning signals coexist in a V2X system. The observable pseudorange values are utilized with the help of derived hyperbola equations during the design of the spoofing detection algorithms. We propose an algorithm, which is named as the sub-optimal search-based spoofing detection algorithm (Algorithm 1), and it considers all possible numbers of spoofing attacking signals, but not all spoofing scenarios with the same amount of spoofing signals. To address the complexity problems based on the increased number of search scenarios of this approach, we propose another algorithm, which is called subset selection-based spoofing detection algorithm (Algorithm 2), with a smart selection of the search subsets. Both of these algorithms are first compared with fixed detection thresholds, which are determined with the Pareto front approach. Then, the performance of the algorithms is investigated when vehicle mobility and spoofing imperfection are considered. Finally, a supervised learning-based decision tree machine learning (ML) algorithm is run without specifying any detection threshold. The results indicate that Algorithm 1 provides higher detection rates than the subset selection-based algorithm;however, the false alarm ratios of Algorithm 2 are much lower than its original performance.

关键词： Vehicle-to-everything Global navigation satellite system Global Positioning System detection algorithms Mathematical models Machine learning algorithms Satellites Global positioning system satellite navigation systems spoofing communication system security detection algorithms

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Concept Drift detection Delay Index

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 4585-4597页

作者： Liu, Anjin Lu, Jie Song, Yiliao Xuan, Junyu Zhang, Guangquan Univ Technol Sydney AAII Ultimo NSW 2007 Australia

Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models. Many drift detection methods apply thresholds to distinguish between drift or non-drift streams and to claim their method outperforms others with non-aligned drift thresholds. We consider that selecting a proper drift threshold could be more important than developing a new drift detection algorithm, and different drift detection algorithms may end up with very similar performance with aligned drift thresholds. To better understand this process, we propose a novel threshold selection algorithm to align the drift thresholds of a set of algorithms so that they are all at the same sensitivity level. Based on comprehensive experiment evaluations, we observed that several state-of-the-art drift detection algorithms could achieve similar results by aligning their thresholds, providing a novel insight to explain how drift detection algorithms contribute to data stream learning. We noticed that a higher detection sensitivity improves accuracy for data streams with frequent distribution change. The evaluation results are showing that drift thresholds should not be fixed during stream learning. Rather, they should adjust dynamically based on the prevailing conditions of the data stream.

关键词： detection algorithms Error analysis Indexes Robustness Training Monitoring Data models Data stream data mining concept drift machine learning

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CenterPoint-SE: A Single-Stage Anchor-Free 3-D Object detection Algorithm With Spatial Awareness Enhancement

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 10760-10773页

作者： Wang, Hai Tao, Le Cai, Yingfeng Chen, Long Li, Yicheng Sotelo, Miguel Angel Li, Zhixiong Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Univ Alcal Dept Comp Engn Alcal De Henares Spain Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Opole Univ Technol Fac Mech Engn Opole Poland

Real-time and accurate 3-D object detection is one of the foundational technologies for environmental perception in autonomous vehicles. However, the existing second-stage anchor-based 3-D object detection algorithms have high accuracy, but they are challenging in terms of computation complexity and latency. Due to poor perception of spatial features, the accuracy of the existing single-stage anchor-free detection algorithms with low latency are difficult to be implemented into autonomous vehicles. Therefore, we focus on enhancing the spatial perception ability of the anchor-free detection network based on CenterPoints. In this paper, we propose a single-stage anchor-free 3-D object detector CenterPoint-Space-Enhancement (CenterPoint-SE) algorithm and construct an efficient 3-D backbone network to extract fine-grained spatial geometric features by introducing a spatial attention mechanism and residual structure. At the same time, a powerful spatial semantic feature fusion module, the enhancement of feature fusion (EF-Fusion), is designed. In addition, we add a lightweight IoU prediction branch to improve the algorithm's perception of various object sizes. Finally, we add a foreground point segmentation auxiliary training branch to enable the 3-D backbone to obtain object boundary features. We use the ONCE dataset to train and validate the proposed model, and the results showed that the proposed CenterPoint-SE achieves 70.33 mAP and an inference speed of 17.15 FPS, outperforming other methods.

关键词： Feature extraction Object detection Point cloud compression Training Location awareness Autonomous vehicles detection algorithms 3D object detection space feature enhancement single-stage anchor-free point cloud

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Anomaly detection algorithms for the Sleeping Cell detection in LTE Networks

Anomaly Detection Algorithms for the Sleeping Cell Detection...

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IEEE Conference on Vehicular Technology (VTC)

作者： Sergey Chernov Michael Cochez Tapani Ristaniemi Department of Mathematical Information Technology University of Jyväskylä Finland

The Sleeping Cell problem is a particular type of cell degradation in Long-Term Evolution (LTE) networks. In practice such cell outage leads to the lack of network service and sometimes it can be revealed only after multiple user complains by an operator. In this study a cell becomes sleeping because of a Random Access Channel (RACH) failure, which may happen due to software or hardware problems. For the detection of malfunctioning cells, we introduce a data mining based framework. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving Base Station (BS). The crucial element of the developed framework is an anomaly detection algorithm. We compare performances of distance, centroid distance and probabilistic based methods, using Receiver Operating Characteristic (ROC) and Precision-Recall curves. Moreover, the theoretical comparison of the methods' computational efficiencies is provided. The sleeping cell detection framework is verified by means of a dynamic LTE system simulator, using Minimization of Drive Testing (MDT) functionality. It is shown that the sleeping cell can be pinpointed.

关键词： detection algorithms Training Testing Histograms Data mining Probabilistic logic Mobile communication

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Multi-Objective Optimization of Local Overlapping Community detection: A Formal Model and Novel Evolutionary Algorithm

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第4期10卷 2124-2140页

作者： Zhang, Lei Li, Bin Yang, Haipeng Cheng, Fan Zhang, Cheng Cao, Renzhi Anhui Univ Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Key Lab Intelligent Comp & Signal ProcMinist Educ Hefei 230039 Peoples R China Pacific Lutheran Univ Sch Comp Sci Tacoma WA 98447 USA

Local overlapping community detection (LOCD), which aims to discover the multiple communities containing a given starting node, becomes a hot research problem in community detection area. However, existing LOCD methods only consider the quality of each local community containing the starting node, while ignoring the information between the multiple local communities. To this end, in this paper, we first formulate a novel multi-objective model to better assess the quality of local overlapping communities, where two novel objectives are suggested by fully utilizing the information within and between the multiple local communities. Afterward, we propose a tri-division representation-based multi-objective evolutionary algorithm (TDR-MOEA) for effectively finding multiple local overlapping communities for a given node under the formulated multi-objective model. In TDR-MOEA, a tri-division individual representation scheme is designed to effectively encode and decode the local overlapping communities. Based on the representation, two novel crossover and mutation operators are then suggested. In addition, an effective population initialization strategy is designed to obtain an initial population with better diversity and convergence. The effectiveness of the proposed multi-objective model and TDR-MOEA is verified on several synthetic networks and real-world networks. The experimental results show that TDR-MOEA is superior to state-of-the-art algorithms for solving LOCD.

关键词： Heuristic algorithms Optimization Linear programming Clustering algorithms detection algorithms Social networking (online) Evolutionary computation Community detection individual representation local overlapping community detection multi-objective evolutionary algorithm (MOEA)

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MSND: Modified Standard Normal Deviate Incident detection Algorithm for Connected Autonomous and Human-Driven Vehicles in Mixed Traffic

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第11期24卷 13151-13160页

作者： Gokasar, Ilgin Timurogullari, Alperen Ozkan, Sarp Semih Deveci, Muhammet Lv, Zhihan Bogazici Univ Dept Civil Engn Bebek Turkey Imperial Coll London Royal Sch Mines London SW7 2AZ England Natl Def Univ Dept Ind Engn Turkish Naval Acad TR-34940 Istanbul Turkey Uppsala Univ Fac Arts Dept Game Design S-62167 Visby Sweden

Advances in IoT and IoV technology have made connected autonomous vehicles (CAVs) data sources. Using CAVs as data sources and in incident management algorithms can create faster, more reliable, and more effective algorithms. This paper proposes a modified standard normal deviation (MSND) incident detection algorithm that uses CAVs as data sources and considers multiple traffic parameters. MSND is utilized in conjunction with two other incident detection algorithms, Standard Normal Deviation (SNS) and California (CAL), in a method of incident management known as Variable Speed Limits (VSL). SUMO Traffic Simulation Software is used to evaluate the effectiveness of the proposed method. A 10.4-kilometer road network is developed. Numerous scenarios are simulated on this road network, with variables including traffic demand, autonomous vehicle penetration rate, incident location, incident length, and incident lane. On the effectiveness metrics of detection rate, false alarm rate, and mean time to detect, simulation results demonstrate that the proposed method outperforms the SND and California methods. In terms of detection rate, the MSND algorithm performs the best, with a 12.27% improvement over the SND algorithm and a 21.99% improvement over the California method. After integrating all incident detection algorithms with the VSL traffic management method and simulating each combination, it was determined that the MSND-VSL integration reduced average density in the critical region by 19.73 percent, followed by SND-VSL with a 13.94 percent reduction and CAL-VSL with a 9.9 percent reduction.

关键词： detection algorithms Standards Roads Autonomous vehicles Soft sensors Meters Prediction algorithms Connected autonomous vehicles incident detection algorithms SUMO

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Low Complexity CPM detection Algorithm With the g-th Power Based on the LD-RD

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IEEE COMMUNICATIONS LETTERS 2023年第3期27卷 886-890页

作者： Zhang, Jintao Gong, Kexian Liu, Zilong Sun, Peng Wang, Wei Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Based on the Laurent decomposition (LD) and the Rimoldi decomposition (RD) with the reduced-search tech-nique, we propose a low-complexity continuous phase modulation (CPM) detection algorithm through the operation of the g-th power. In this way, the denominator of the modulation index is effectively equivalent to a smaller value, achieving the usage of much fewer trellis states in the Viterbi algorithm (VA). Finally, simulation results show that, compared to the optimal detection method, the proposed algorithms significantly reduce the complexity with negligible bit error rate (BER) loss.

关键词： Complexity theory Mathematical models Modulation detection algorithms Simulation Symbols Receivers Continuous phase modulation Laurent decomposition low complexity Viterbi algorithm Rimoldi decomposition

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Concurrent Transmission and Multiuser detection of LoRa Signals

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IEEE INTERNET OF THINGS JOURNAL 2023年第1期10卷 774-786页

作者： Nguyen, The Khai Nguyen, Ha H. Bedeer, Ebrahim Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK Canada

This article investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing a group of multiple end devices (EDs) to communicate with multiple multi-antenna gateways simultaneously (i.e., in the same time slot) on the same frequency band and using the same spreading factor. The maximum-likelihood (ML) decision rule is first derived for noncoherent detection of information bits transmitted by multiple devices in a group. To overcome the high complexity of the ML detection, we propose a suboptimal two-stage detection algorithm to balance the computational complexity and error performance. In the first stage, we identify transmitted chirps (without knowing which EDs transmit them). In the second stage, we determine the EDs that transmit the specific chirps identified from the first stage. To improve the detection performance in the second stage, we also optimize the transmit powers of EDs to minimize the similarity, measured by the Jaccard coefficient, between the received powers of any pair of EDs in the same group. As the power control optimization problem is nonconvex, we use concepts from successive convex approximation to transform it to an approximate convex optimization problem that can be solved iteratively and guaranteed to reach a suboptimal solution. Simulation results demonstrate and justify the tradeoff between transmit power penalties and network scalability of the proposed LoRa network model. In particular, by grouping two or three EDs in each group for concurrent transmission, the uplink capacity of the proposed network can be doubled or tripled over that of a conventional LoRa network, albeit at the expense of additional 3.0 or 4.7 dB transmit power.

关键词： Modulation Chirp Multiuser detection detection algorithms Detectors Multiaccess communication Scalability Chirp-spread spectrum modulation Internet-of-Things LoRa LoRaWAN multiuser detection (MUD) noncoherent detection power control

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A Fast and High-Precision Ellipse detection Method Based on the Candy's Theorem

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IEEE ACCESS 2023年 11卷 102971-102980页

作者： Fang, Xiujun Zhang, Enzheng Li, Bingchen Zhai, Bin Zhejiang Sci Tech Univ Sch Mat Sci & Engn Hangzhou 310018 Peoples R China

Ellipse detection is one of the important parts of intelligent manufacturing, and the efficient detection of elliptical contours corresponding to hole parts is the prerequisite before performing practical applications. Aiming at enhancing the efficiency in ellipse detection, a fast and high-precision ellipse detection method based on the Candy's theorem is proposed, in which the constrained simple polygon constraint and the Candy's theorem constraint are used to improve the efficiency of ellipse detection. The theory of the proposed ellipse detection method is described in detail. The comparison experiments with six popular ellipse detection methods are performed based on three public datasets, which are to verify the ellipse detection ability of the proposed method in improving detection speed and precision. And a small hole detection experiment is performed with the constructed experimental setup, which is to verify the effectiveness of the proposed method to detect small holes in practical applications. The proposed method can provide referenced methods and theories in the field of ellipse detection.

关键词： Turning Image edge detection Fitting Transforms Image segmentation Feature extraction Manufacturing detection algorithms The Candy's theorem ellipse detection detection speed detection precision

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