IEEE Transactions on Intelligent Vehicles最新文献_第10页

Transit Signal Priority Strategy With Heterogeneous Graph-Based Deep Reinforcement Learning for Autonomous Public Transit Vehicles 基于异构图深度强化学习的自动公交信号优先策略

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-03 DOI: 10.1109/TIV.2024.3445299

Bin Zhou;Simon Hu;Yuanbo Yang;Xiaoxiang Na;Jose Escribano;Dongfang Ma;Sheng Jin;Bugao Zhang;Der-Horng Lee

{"title":"Transit Signal Priority Strategy With Heterogeneous Graph-Based Deep Reinforcement Learning for Autonomous Public Transit Vehicles","authors":"Bin Zhou;Simon Hu;Yuanbo Yang;Xiaoxiang Na;Jose Escribano;Dongfang Ma;Sheng Jin;Bugao Zhang;Der-Horng Lee","doi":"10.1109/TIV.2024.3445299","DOIUrl":"https://doi.org/10.1109/TIV.2024.3445299","url":null,"abstract":"Rapid advancements in vehicle communication and autonomous driving technologies have led to the emergence of Autonomous Public Transit Vehicles (APTVs), playing a pivotal role in enhancing the efficiency of on-demand flexible-route transit services. One promising approach to further optimize these services and alleviate urban traffic congestion is the development of smarter Transit Signal Priority (TSP) strategies. This paper proposes a decentralized intelligent traffic signal control algorithm based on Deep Reinforcement Learning (DRL), tailored for the TSP strategy supporting flexible-route transit services. Our algorithm can accommodate various road network structures and APTV penetration rates, ensuring extensive applicability. Specifically, it employs a heterogeneous graph model to capture diverse information, including network topologies and dynamic characteristics of APTVs. Through extensive testing in multiple scenarios across varied road networks and traffic conditions, our algorithm has consistently outperformed both traditional traffic control methods and state-of-the-art DRL-based methods. Furthermore, it demonstrates effective zero-shot transferability, adapting to real-world scenarios without additional training.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 3","pages":"2174-2192"},"PeriodicalIF":14.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coaxial Tilt-Rotor UAV: Fixed-Time Control, Mixer, and Flight Test 同轴倾转旋翼无人机：固定时间控制、混频器和飞行试验

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-02 DOI: 10.1109/TIV.2024.3453208

Zongyang Lv;Yi Sun;Shengming Li;Yuhu Wu;Xi-Ming Sun

引用次数: 0

Hierarchical Event-Triggered Control for CAV Platoons Using Adaptive Neural Network 基于自适应神经网络的CAV队列分层事件触发控制

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-02 DOI: 10.1109/TIV.2024.3453118

Longwang Huang;Bingxin Xie;Hang Zhao;Yongfu Li

{"title":"Hierarchical Event-Triggered Control for CAV Platoons Using Adaptive Neural Network","authors":"Longwang Huang;Bingxin Xie;Hang Zhao;Yongfu Li","doi":"10.1109/TIV.2024.3453118","DOIUrl":"https://doi.org/10.1109/TIV.2024.3453118","url":null,"abstract":"In this paper, a hierarchical event-triggered platoon control framework is developed for connected and automated vehicle (CAV) platoons with unknown dynamics and communication resource limitation. Firstly, a two-layer model that consists of an upper-layer model and a lower-layer model is introduced to depict the longitudinal kinematic and dynamic characterizes of CAVs in the platoon. Secondly, a double-layer event-triggered platoon control design framework is proposed for the considered CAV platoon. In the upper-layer, an event-triggered platoon controller is designed to generate desired platoon trajectory for CAV. A compensation signal is properly defined such that the platoon controller allows the spacing policy of the CAVs switching among constant spacing, constant time-headway, and variable time-headway policies. In the lower-layer, an adaptive neural network sliding-mode controller is developed to force the CAVs to track desired trajectory. An event-triggering condition that made up of data transmission error and vehicle consensus error is designed to determine data transmission sequence, such that a balance can be achieved between data transmission and platoon control performance. Finally, theoretical analysis is presented to illustrate CAV state stability and platoon string stability, and simulation studies are carried out to further demonstrate feasibility and effectiveness of the scheme.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3398-3408"},"PeriodicalIF":14.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anti-Jamming Attack Mixed Strategy for Formation Tracking Control via Game-Theoretical Reinforcement Learning 基于博弈论强化学习的编队跟踪控制抗干扰攻击混合策略

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-30 DOI: 10.1109/TIV.2024.3452483

Lei Xue;Bei Ma;Yongbao Wu;Jian Liu;Chaoxu Mu;Donald C. Wunsch

{"title":"Anti-Jamming Attack Mixed Strategy for Formation Tracking Control via Game-Theoretical Reinforcement Learning","authors":"Lei Xue;Bei Ma;Yongbao Wu;Jian Liu;Chaoxu Mu;Donald C. Wunsch","doi":"10.1109/TIV.2024.3452483","DOIUrl":"https://doi.org/10.1109/TIV.2024.3452483","url":null,"abstract":"Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, we suppose there are three types of UAVs: leaders, followers, and jammers. To illustrate the interactions between multiple UAVs of the formation tracking process under jamming attack, a three-layer Stackelberg game is constructed. Leaders and followers need to resist interference from jammers during the formation tracking process with a leader-follower structure. Leaders and followers achieve formation tracking through cooperation. The interactions between jammers and the other UAVs form a non-cooperative game. Moreover, the utility functions of a mixed strategy, which contain mobility and communication strategies, are designed for the three-layer Stackelberg game. The Stackelberg-Nash equilibrium of the designed game model is proven to exist. For seeking the Stackelberg-Nash equilibrium, a tri-level actor-critic (Tri-AC) reinforcement learning algorithm is designed. The convergence of the designed algorithm is also proved theoretically. Finally, the effectiveness of the designed method is verified by various simulation experiments.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3382-3397"},"PeriodicalIF":14.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cooperative Communication via Automated Guided Vehicle and Unmanned Aerial Vehicle: A Distributed Collaborate Beamforming Method 自动制导飞行器与无人机协同通信：一种分布式协同波束形成方法

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-30 DOI: 10.1109/TIV.2024.3452157

Shuang Liang;Minghao Yin;Geng Sun;Jiahui Li;Hongjuan Li;Jiacheng Wang;Dusit Niyato;Victor C. M. Leung

{"title":"Cooperative Communication via Automated Guided Vehicle and Unmanned Aerial Vehicle: A Distributed Collaborate Beamforming Method","authors":"Shuang Liang;Minghao Yin;Geng Sun;Jiahui Li;Hongjuan Li;Jiacheng Wang;Dusit Niyato;Victor C. M. Leung","doi":"10.1109/TIV.2024.3452157","DOIUrl":"https://doi.org/10.1109/TIV.2024.3452157","url":null,"abstract":"The advantages of autonomy, stability, and high load capability make automated guided vehicles (AGVs) appealing for applications like intelligent transportation networks. Nevertheless, AGVs may face limitations in terms of flexibility and transmission efficiency. In this paper, we consider the scenario where unmanned aerial vehicles (UAVs) serve dual roles in enhancing the communications of AGVs. Specifically, one group of UAVs is employed to support AGVs in data transmission, while another group of UAVs equipped with computational resources, functions as aerial base stations (ABSs) for receiving and processing the collected data. Following this, we explore the collaborative deployment between AGVs and UAVs, and propose a highly efficient, low-interference, and energy-efficient uplink data transmission framework based on distributed collaborative beamforming. Correspondingly, we formulate a high-performance and low-interference transmission multi-objective optimization problem (HLTMOP) to reduce the transmission time and operation energy consumption of the AGVs and UAVs, while minimizing the total sidelobe levels toward the directions of all non-current receiving ABSs. Due to the NP-hardness of the HLTMOP, we propose a swarm intelligence algorithm, namely, improved multi-objective ant lion optimization (IMOALO), with three improved operators. Simulation results show that the proposed IMOALO algorithm performs better and can generate more excellent solutions than other benchmark algorithms.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3368-3381"},"PeriodicalIF":14.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Peer-to-Peer Personalized Federated Transfer Learning for Battery State of Health Estimation of Vehicles 汽车电池健康状态估计的点对点个性化联邦迁移学习

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-29 DOI: 10.1109/TIV.2024.3449334

Tianjing Wang;ZhaoYang Dong

{"title":"Peer-to-Peer Personalized Federated Transfer Learning for Battery State of Health Estimation of Vehicles","authors":"Tianjing Wang;ZhaoYang Dong","doi":"10.1109/TIV.2024.3449334","DOIUrl":"https://doi.org/10.1109/TIV.2024.3449334","url":null,"abstract":"The enhancement of battery reliability and safety during operational usage for electric vehicles has been a primary focus of state-of-the-art research, leading to the development of various data-driven technologies for battery state-of-health (SOH) estimation. Nevertheless, these approaches, rooted in traditional centralized computing paradigm, grapple with conflicts related to data needs versus privacy protection, robustness versus personalization, and transferability versus accuracy. To address these challenges, this study proposes a novel decentralized federated transfer learning (FTL) method, named P2P-PerFTL, which aggregates local SOH estimation models into a global model using peer-to-peer communication while preserving battery data locally, combining federated learning and transfer learning to realize personalization and transferability within various working conditions and batteries. This algorithm utilizes a domain-shift-based weighted aggregation mechanism for global model formulation, and constructs a personalized and transferable neural network architecture by assigning part layers as personalization layers and incorporating domain shift loss. Through a comprehensive case study with four FTL scenarios, it is demonstrated that the proposed P2P-PerFTL can train a highly proficient SOH estimation model with a limited volume of data from local clients across diverse operational conditions and battery types, and outperforms alternative training frameworks.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3195-3207"},"PeriodicalIF":14.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Barrier-Function-Enabled Control for Vessel Systems Under Dynamic Event-Triggered Protocols: The ADP Approach 动态事件触发协议下船舶系统的障碍功能控制：ADP方法

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-29 DOI: 10.1109/TIV.2024.3451517

Xueli Wang;Tianzhen Wang;Derui Ding

{"title":"Barrier-Function-Enabled Control for Vessel Systems Under Dynamic Event-Triggered Protocols: The ADP Approach","authors":"Xueli Wang;Tianzhen Wang;Derui Ding","doi":"10.1109/TIV.2024.3451517","DOIUrl":"https://doi.org/10.1109/TIV.2024.3451517","url":null,"abstract":"In this paper, a constraint-aware suboptimal control scheme under adaptive dynamic programming (ADP) is investigated for nonlinear vessel systems characterized by dynamic event-triggering mechanisms (DETMs) and state constraints. A relaxed barrier function (RBF) is designed as a penalty term in the cost function to replace the inequality constraints, thus achieving constraints on the system state. By resorting to neural network (NN) approximation of the nonlinear dynamics, an optimal control framework is established via the dual method, combined with the Lagrange multipliers on the RBF. Within this framework, the Lagrangian multipliers are employed to balance the optimisation of control performance and state constraints. The convergence of value iteration algorithms is revealed through rigorously mathematical analysis. Furthermore, by using the Lyapunov stability theory, the desired observer gain is calculated via a set of matrix inequalities, and sufficient conditions for the relevant parameters and learning rates are derived such that the weight estimation errors of both the observer's NNs and the actor-critic NNs are ultimately bounded. Finally, simulations of both vessel systems and numerical examples are used to validate the effectiveness of the proposed method.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3344-3354"},"PeriodicalIF":14.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple Aerial Videos-Based Long-Distance Vehicle Trajectory Construction With Spatiotemporal Continuity 基于多航拍视频的时空连续性远程车辆轨迹构建

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-29 DOI: 10.1109/TIV.2024.3451722

Yi He;Bo Cao;Ching-Yao Chan;Ye Li;Xin Xia;Helai Huang

{"title":"Multiple Aerial Videos-Based Long-Distance Vehicle Trajectory Construction With Spatiotemporal Continuity","authors":"Yi He;Bo Cao;Ching-Yao Chan;Ye Li;Xin Xia;Helai Huang","doi":"10.1109/TIV.2024.3451722","DOIUrl":"https://doi.org/10.1109/TIV.2024.3451722","url":null,"abstract":"The high-fidelity trajectory data extracted from aerial videos can provide reliable trajectory source for traffic-related researches and promote autonomous driving researches. However, compared to the current studies focusing on the single video-based trajectory extraction, the long-distance trajectory extracted from multiple aerial videos still suffer from trajectory unrealistic and discontinuity problems. Existing long-distance trajectory construction method can be categorized into two classes: trajectory-based and video-based methods. The former significantly changes the original trajectory data, leading to trajectory distortion and cannot restore vehicle travel videos with corresponding trajectories, thus falling short of meeting research demands in traffic filed. The trajectory data extracted by the latter approach still exhibit noticeable discontinuity at videos stitching area, leading to the biases of vehicle motion parameters, which further cause the errors of studies such as vehicle model calibration and safety evaluation. Therefore, this study introduces a novel long-distance trajectory construction method which can extract long-distance trajectories from multiple aerial videos and mitigate the discontinuity problem in video stitching area. The proposed method contains four modules which are multi-video registration, vehicle detection and tracking, long-distance trajectory connection and fusion, trajectory smoothing and parameters extraction. Experimental results show that the proposed method can obtain more continuous motion parameters in spatiotemporal distribution, and have more advantages in traffic-related studies. This work may provide a new research perspective for current trajectory construction researches in traffic field.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3355-3367"},"PeriodicalIF":14.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Driving in Underground Mines via Parallel Driving Operation Systems: Challenges, Frameworks and Cases Study 基于并行驾驶操作系统的地下矿山自动驾驶：挑战、框架和案例研究

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-28 DOI: 10.1109/TIV.2024.3450609

Bin Tian;Jing Yang;Caiji Zhang;Xuedi Hao;Shi Meng;Shibin Wang;Zheng Yang;Long Chen;Yanlong Zhao;Shirong Ge

引用次数: 0

Motion-Based Camera-LiDAR Online Calibration With 3D Camera Ground 基于运动的相机-激光雷达在线标定与3D相机地面

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-28 DOI: 10.1109/TIV.2024.3451058

Dongkyu Lee;Eun-Jung Bong;Seok-Cheol Kee

{"title":"Motion-Based Camera-LiDAR Online Calibration With 3D Camera Ground","authors":"Dongkyu Lee;Eun-Jung Bong;Seok-Cheol Kee","doi":"10.1109/TIV.2024.3451058","DOIUrl":"https://doi.org/10.1109/TIV.2024.3451058","url":null,"abstract":"This paper addresses a novel camera-LiDAR online targetless calibration method. As the installation of heterogeneous sensors in autonomous vehicles is increasing, the importance of sensor calibration is also growing. An automatic sensor calibration function is essential for safe autonomous driving, responding to the sensor's geometric change. In this paper, background knowledge of sensor calibration is explained, and related papers are referenced. Depth estimation and semantic segmentation models are learned to detect significant regions from the camera using open and custom datasets. This paper describes the online calibration procedures based on motion-based calibration between the camera and LiDAR. Motion-based calibration faces a challenge for lack of roll and pitch variation, and our method overcomes this challenge by estimating a camera 3D ground plane from semantic segmentation and depth estimation and simultaneously detecting a 3D LiDAR ground plane. LiDAR-to-camera roll, pitch, and z values are then extracted using the camera and LiDAR ground plane, and the motion-based calibration problem is solved and optimized using those values as constraints. Experimental results conducted on urban roads and the proving ground C-track using our autonomous vehicles showed that the proposed method is quantitatively and qualitatively improving the existing method.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3278-3290"},"PeriodicalIF":14.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0