IEEE Open Journal of Intelligent Transportation Systems最新文献_第3页

Enhancing Vehicular Network Efficiency: The Impact of Object Data Inclusion in the Collective Perception Service 提高车载网络效率：将对象数据纳入集体感知服务的影响

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-08-01 DOI: 10.1109/OJITS.2024.3437206

Andreia Figueiredo;Pedro Rito;Miguel Luís;Susana Sargento

{"title":"Enhancing Vehicular Network Efficiency: The Impact of Object Data Inclusion in the Collective Perception Service","authors":"Andreia Figueiredo;Pedro Rito;Miguel Luís;Susana Sargento","doi":"10.1109/OJITS.2024.3437206","DOIUrl":"10.1109/OJITS.2024.3437206","url":null,"abstract":"As the automotive industry evolves, integrating intelligent technologies and cooperative services in vehicular networks has become crucial to enhance road safety and autonomous driving capabilities. However, this integration can strain networks, particularly when exchanging a high volume of object information. This work studies the impact of the Collective Perception Messages (CPMs) size on the vehicular network performance. We introduce an algorithm aimed at optimizing the efficiency of extra object data inclusion in CPMs. The focus is on evaluating the vehicular network efficiency by selectively including extra objects within the available message space, strategically enhancing the transmission of more objects. This optimization not only reduces the need for constant CPM generation, but also maximizes the efficiency of information exchange. Using real-world vehicular data, this approach’s effectiveness in improving the Collective Perception Service (CPS) is demonstrated, showing a significant improvement when compared to traditional CPS standard: the proposed algorithm is capable of transmitting 14% more object information while using 2.6% fewer bytes. In addition, if we were to maintain the same number of bytes used in transmission as the CPS standard, our algorithm would result in a 23% increase in transmitted object information. Furthermore, the additional delay incurred by the algorithm is minimal, with an average of just 3 ms.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"454-468"},"PeriodicalIF":4.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Diagnostic Technique for AI-Enabled System 人工智能系统的实时诊断技术

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-30 DOI: 10.1109/OJITS.2024.3435712

Hiroaki Itsuji;Takumi Uezono;Tadanobu Toba;Subrata Kumar Kundu

{"title":"Real-Time Diagnostic Technique for AI-Enabled System","authors":"Hiroaki Itsuji;Takumi Uezono;Tadanobu Toba;Subrata Kumar Kundu","doi":"10.1109/OJITS.2024.3435712","DOIUrl":"10.1109/OJITS.2024.3435712","url":null,"abstract":"The last few decades have witnessed a dramatic evolution of Artificial Intelligence (AI) algorithms, represented by Deep Neural Networks (DNNs), resulting in AI-enabled systems being significantly dominant in various fields, including robotics, healthcare, and mobility. AI-enabled systems are currently used even for safety-critical applications, including automated driving, where they encounter reliability challenges from both hardware (HW) and software (SW) perspectives. However, there is no effective technique available that can diagnose HW and SW of AI-enabled systems in real-time during operation. Therefore, this paper proposes an intelligent real-time diagnostic technique for detecting HW and SW anomalies of AI-enabled systems and continuously improving the SW quality during operation. The proposed technique can detect HW anomalies to avoid unexpected changes in AI parameters and subsequent AI performance degradation using single context data with a detection accuracy of more than 92%. The proposed technique can also detect SW anomalies and identify edge cases in real-time, which could result in performance degradation by more than 50% compared to normal conditions. The identified edge cases can be used to continuously enhance the SW quality. Experimental results show the effectiveness of the technique for practical applications and thus can contribute to realize reliable and improved AI-enabled systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"483-494"},"PeriodicalIF":4.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficiently Modeling Lateral Vehicle Movement Including its Temporal Interrelations Using a Two-Level Stochastic Model 利用两级随机模型有效模拟车辆横向移动及其时间相互关系

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-29 DOI: 10.1109/OJITS.2024.3435078

N. Neis;J. Beyerer

{"title":"Efficiently Modeling Lateral Vehicle Movement Including its Temporal Interrelations Using a Two-Level Stochastic Model","authors":"N. Neis;J. Beyerer","doi":"10.1109/OJITS.2024.3435078","DOIUrl":"10.1109/OJITS.2024.3435078","url":null,"abstract":"The lateral movement of vehicles within their lane under homogeneous traffic conditions is decisive for the range of vision of vehicle sensors. It significantly contributes to the maximum situational awareness an automated driving function can achieve. Given the integral role that simulations play in the validation of automated driving functions, the development of models that accurately describe the lateral movement of vehicles within their lane becomes essential. A few models have already been proposed in literature that address this task. Existing models, however, exhibit limitations when it comes to their usage for the virtual validation of automated driving functions such as the replication of general instead of driver-specific behavior and complex calibration methods. Furthermore, the metrics used for evaluation focus on measuring the accordance of the overall lateral offset and speed distribution and do not take into account the temporal course of the lateral offset profiles. Within this work, we introduce a two-level stochastic model addressing the identified limitations. We further present a strategy suitable for evaluating the low-level characteristics of the generated lateral offset profiles relevant for validating an automated driving function such as a cut-in detection function within simulations. The model’s capabilities are demonstrated based on five single driver datasets. It is shown that the model allows for efficient calibration, is able to replicate the behavior of these drivers, and is characterized by short runtimes. This makes it suitable for the virtual validation of automated driving functions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"566-580"},"PeriodicalIF":4.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10612773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles 基于联合与集合学习的新型互联电动汽车电池健康状况估计方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-22 DOI: 10.1109/OJITS.2024.3430843

Praveen Abbaraju;Subrata Kumar Kundu

{"title":"A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles","authors":"Praveen Abbaraju;Subrata Kumar Kundu","doi":"10.1109/OJITS.2024.3430843","DOIUrl":"10.1109/OJITS.2024.3430843","url":null,"abstract":"Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"445-453"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications FedRSC：针对多标签路面分类的联合学习分析

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-22 DOI: 10.1109/OJITS.2024.3432176

Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos

{"title":"FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications","authors":"Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos","doi":"10.1109/OJITS.2024.3432176","DOIUrl":"10.1109/OJITS.2024.3432176","url":null,"abstract":"The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"433-444"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10606293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameters Estimation of a Microscopic Traffic Flow Sub-Model Within a Multiscale Approach Using Experimental Data 在多尺度方法中利用实验数据估算微观交通流子模型的参数

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-15 DOI: 10.1109/OJITS.2024.3427790

Facundo Storani;Roberta Di Pace;Shi-Teng Zheng;Rui Jiang;Stefano de Luca

{"title":"Parameters Estimation of a Microscopic Traffic Flow Sub-Model Within a Multiscale Approach Using Experimental Data","authors":"Facundo Storani;Roberta Di Pace;Shi-Teng Zheng;Rui Jiang;Stefano de Luca","doi":"10.1109/OJITS.2024.3427790","DOIUrl":"10.1109/OJITS.2024.3427790","url":null,"abstract":"Future traffic contexts will likely involve the coexistence of human-driven vehicles and connected and automated vehicles (CAVs). To assess the impact of CAVs, especially in large-scale applications, intermediate hybrid multi-scale models can be used. These models are easily adaptable to traffic control strategies by employing disaggregated modeling in regions where such strategies are implemented and macroscopic modeling in other regions indirectly affected by the controlled infrastructure. This paper focuses on a model previously established in the literature, the H - CA&CTM (Hybrid Cellular Automata -CA- Cell Transmission Model-CTM), with an emphasis on the micro model that can be implemented in the hybrid traffic flow model. The research has two primary aims: 1) Investigate the calibration of the CA model with respect to various cell lengths using two distinct approaches: simulating all vehicles together in a closed ring layout and simulating each vehicle using data obtained from its respective follower; 2) Utilize vehicle trajectory data for the calibration procedure, enabling a comprehensive comparison of methods. Two detailed approaches were considered: 1. Measured Leader – Simulated Follower interaction approach. 2. Simulated Leader – Simulated Follower interaction approach. The major finding of the paper is that the calibrated parameters obtained using the Simulated Leader approach display greater regularity across different cell lengths.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"409-421"},"PeriodicalIF":4.6,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10597615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Managing Risk in the Design of Modular Systems for an Autonomous Shuttle 自主航天飞机模块化系统设计中的风险管理

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-08 DOI: 10.1109/OJITS.2024.3425165

Thomas H. Drage;Kieran Quirke-Brown;Lemar Haddad;Zhihui Lai;Kai Li Lim;Thomas Bräunl

{"title":"Managing Risk in the Design of Modular Systems for an Autonomous Shuttle","authors":"Thomas H. Drage;Kieran Quirke-Brown;Lemar Haddad;Zhihui Lai;Kai Li Lim;Thomas Bräunl","doi":"10.1109/OJITS.2024.3425165","DOIUrl":"10.1109/OJITS.2024.3425165","url":null,"abstract":"This paper presents an analysis and implementation of a robust autonomous driving system for an electric passenger shuttle in shared spaces. We present results of a risk assessment for our vehicle scenario and develop a flexible architecture that integrates safety features and optimises open-source software, facilitating research and operational functionality. Identifying limitations of the Robot Operating System (ROS) framework, we incorporate our own control measures for autonomous, unsupervised operation with enhanced intelligence. The study emphasizes algorithm selection based on application requirements to ensure optimal performance. We discuss system improvements, including monitoring node implementation and localization algorithm selection. Future work should explore transitioning to a real-time operating system (RTOS) and establishing standardized software engineering practices for consistent reliability. Our findings contribute to effective autonomous shuttle systems in shared spaces, promoting safer and more reliable transportation solutions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"555-565"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance 全局和局部意识：结合强化学习和基于模型的控制来避免碰撞

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-05 DOI: 10.1109/OJITS.2024.3424587

Luman Zhao;Guoyuan Li;Houxiang Zhang

{"title":"Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance","authors":"Luman Zhao;Guoyuan Li;Houxiang Zhang","doi":"10.1109/OJITS.2024.3424587","DOIUrl":"10.1109/OJITS.2024.3424587","url":null,"abstract":"In this research, we focus on developing an autonomous system for multiship collision avoidance. The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. To achieve this, firstly, DRL is used to learn a policy that maps observable states of target ships to a sequence of predicted waypoints. This learning task aims to generate a specific trajectory while avoiding collision with target ships complying with the international regulations for preventing collisions at sea (COLREGs). The learned policy is used as a global path planner during navigation. Secondly, the line-of-sight (LOS) guidance system is applied to calculate the desired course command based on the collision-free trajectory generated according to the policy. Lastly, a model-based control strategy is implemented to control the ship to the specific goal in collision-free space while satisfying the desired commands. We demonstrate the performance of the approach using an example of an autonomous surface vehicle. In comparison to other methods, our proposed control can provide a more stable and smoother maneuvering effect.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"422-432"},"PeriodicalIF":4.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing 基于因子图的规划作为自动驾驶汽车竞赛的推理方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-25 DOI: 10.1109/OJITS.2024.3418956

Salman Bari;Xiagong Wang;Ahmad Schoha Haidari;Dirk Wollherr

{"title":"Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing","authors":"Salman Bari;Xiagong Wang;Ahmad Schoha Haidari;Dirk Wollherr","doi":"10.1109/OJITS.2024.3418956","DOIUrl":"10.1109/OJITS.2024.3418956","url":null,"abstract":"Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimizationbased formulation. We model the planning problem as a probabilistic inference over a factor graph, with factor nodes capturing the joint distribution of motion objectives. By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. The localized design thinking inherent in this formulation ensures that motion objectives depend on a small subset of variables. We exploit the locality feature of the factor graph structure to integrate the minimum curvature path and local planning computations into a unified algorithm. This diverges from the conventional separation of global and local planning modules, where curvature minimization occurs at the global level. The evaluation of the proposed framework demonstrated superior performance for cumulative curvature and average speed across the racetrack. Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"380-392"},"PeriodicalIF":4.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global-Mapping-Consistency-Constrained Visual-Semantic Embedding for Interpreting Autonomous Perception Models 用于解释自主感知模型的全局映射-一致性约束视觉-语义嵌入技术

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-24 DOI: 10.1109/OJITS.2024.3418552

Chi Zhang;Meng Yuan;Xiaoning Ma;Ping Wei;Yuanqi Su;Li Li;Yuehu Liu

{"title":"Global-Mapping-Consistency-Constrained Visual-Semantic Embedding for Interpreting Autonomous Perception Models","authors":"Chi Zhang;Meng Yuan;Xiaoning Ma;Ping Wei;Yuanqi Su;Li Li;Yuehu Liu","doi":"10.1109/OJITS.2024.3418552","DOIUrl":"10.1109/OJITS.2024.3418552","url":null,"abstract":"From the perspective of artificial intelligence evaluation, the need to discover and explain the potential shortness of the evaluated intelligent algorithms/systems as well as the need to evaluate the intelligence level of such testees are of equal importance. In this paper, we propose a possible solution to these challenges: Explainable Evaluation for visual intelligence. Specifically, we focus on the problem setting where the internal mechanisms of AI algorithms are sophisticated, heterogeneous or unreachable. In this case, a latent attribute dictionary learning method with constrained by mapping consistency is proposed to explain the performance variation patterns of visual perception intelligence under different test samples. By jointly iteratively solving the learning of latent concept representation for test samples and the regression of latent concept-generalization performance, the mapping relationship between deep representation, semantic attribute annotation, and generalization performance of test samples is established to predict the degree of influence of semantic attributes on visual perception generalization performance. The optimal solution of proposed method could be reached via an alternating optimization process. Through quantitative experiments, we find that global mapping consistency constraints can make the learned latent concept representation strictly consistent with deep representation, thereby improving the accuracy of semantic attribute-perception performance correlation calculation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"393-408"},"PeriodicalIF":4.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0