IEEE Transactions on Intelligent Transportation Systems最新文献

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY IEEE 智能交通系统学会

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-27 DOI: 10.1109/TITS.2024.3492852

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IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-27 DOI: 10.1109/TITS.2024.3491472

Simona Sacone

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IEEE Intelligent Transportation Systems Society Information 电气和电子工程师学会智能交通系统协会信息

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-27 DOI: 10.1109/TITS.2024.3492872

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SMPC-Based Motion Planning of Automated Vehicle When Interacting With Occluded Pedestrians 基于 SMPC 的自动驾驶汽车与被遮挡行人交互时的运动规划

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-08 DOI: 10.1109/TITS.2024.3465571

Daofei Li;Yangye Jiang;Jiajie Zhang;Bin Xiao

{"title":"SMPC-Based Motion Planning of Automated Vehicle When Interacting With Occluded Pedestrians","authors":"Daofei Li;Yangye Jiang;Jiajie Zhang;Bin Xiao","doi":"10.1109/TITS.2024.3465571","DOIUrl":"https://doi.org/10.1109/TITS.2024.3465571","url":null,"abstract":"Driving in scenarios with occlusion is challenging but common in daily traffic, especially in urban and rural areas. To handle the potential interaction between the ego vehicle and pedestrian that possibly exists but is occluded by front vehicle, a stochastic model predictive control (SMPC)-based motion planning algorithm is proposed in this study. Firstly, a naturalistic driving dataset of vehicle-pedestrian interaction is established, based on which it is found that in the case of pedestrians passing or not, there are significant differences in front vehicle driving behavior. Then, a probability estimation approach for the presence of pedestrians in the occluded area is designed, which can achieve 91.9% accuracy in the naturalistic driving dataset. A phantom pedestrian model is established to quantify the uncertainty in the occluded area, which is further used to construct the chance constraint of the SMPC planning problem. Finally, a naturalistic driving data based simulation and a pedestrian-driver-in-the-loop experiment are carried out to validate the proposed algorithm. Both simulation and experiments show that our algorithm can effectively utilize the perceived information to speculate pedestrian presence beyond sensing range, thereby enabling proactive decisions to achieve safety, comfort and traffic efficiency in vehicle-pedestrian interactions. The proposed framework may find applications in interaction planning problems with uncertainty challenges.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19820-19830"},"PeriodicalIF":7.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736576","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}

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Blockchain-Based EV Constant Function Pricer and Oraclized State of Charge Estimator 基于区块链的电动汽车恒定函数定价器和开放式充电状态估算器

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-07 DOI: 10.1109/TITS.2024.3469890

Jun Zhong;Jianbin Gao;Hu Xia;Bonsu Adjei-Arthur;Daniel Adu Worae;Hairong Lv;Qi Xia

{"title":"Blockchain-Based EV Constant Function Pricer and Oraclized State of Charge Estimator","authors":"Jun Zhong;Jianbin Gao;Hu Xia;Bonsu Adjei-Arthur;Daniel Adu Worae;Hairong Lv;Qi Xia","doi":"10.1109/TITS.2024.3469890","DOIUrl":"https://doi.org/10.1109/TITS.2024.3469890","url":null,"abstract":"The increasing adoption of Electric Vehicle (EV) systems necessitates the development of an Energy Market structure that facilitates peer-to-peer energy sharing among multiple EVs and entities while ensuring a self-regulating pricing mechanism. Real-time State of Charge (SoC) estimation is critical to meeting the dynamic energy demands of EV systems. In this study, we propose a blockchain-based automated market maker (AMM) that utilizes constant function products to establish an effective self-regulating pricing system for EV energy market prices. Our unique State of Charge estimation system leverages blockchain-based oracles to efficiently handle requests and monitor EV-oriented energy markets. This enables precise monitoring of battery states and achieves improved SoC values through the interior point method. Experimentation on a blockchain network reveals cost-effective energy regulation within EV systems and enhanced SoC estimation predictability within Energy Markets. All contracts undergo rigorous testing and are deployed at a gas cost of \u0000<inline-formula> <tex-math>$2.1913742~x 10^{7}$ </tex-math></inline-formula>\u0000 Wei. Our approach demonstrates high efficiency, for all designed protocols, affirming the efficacy of our proposal. By implementing our blockchain-based AMM and State of Charge estimation system, we ensure transparent and self-regulated energy distribution and pricing within EV Markets, fostering the advancement of autonomous EV systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"21769-21782"},"PeriodicalIF":7.9,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736276","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}

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Energy-Efficient Symbiotic UAV-Enabled MEC Networks via RIS: Joint Trajectory and Phase-Shift Control Optimization

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-06 DOI: 10.1109/TITS.2024.3433382

Pinwei Yang;Xiaoheng Deng;Leilei Wang;Siyu Lin;Jinsong Gui;Xuechen Chen;Shaohua Wan;Yurong Qian

{"title":"Energy-Efficient Symbiotic UAV-Enabled MEC Networks via RIS: Joint Trajectory and Phase-Shift Control Optimization","authors":"Pinwei Yang;Xiaoheng Deng;Leilei Wang;Siyu Lin;Jinsong Gui;Xuechen Chen;Shaohua Wan;Yurong Qian","doi":"10.1109/TITS.2024.3433382","DOIUrl":"https://doi.org/10.1109/TITS.2024.3433382","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) can be employed as short-term aerial base stations or as access points for User Equipments (UEs) to communicate with other UEs effectively. However, communication links may be obstructed by buildings, leading to poor data transfer performance and significant energy consumption. Deploying Reconfigurable Intelligent Surfaces (RIS) as part of the UAV-assisted communication system proves to be an effective means to avoid building obstructions and enhance wireless information quality. However, the complexity of communication relationships in multi-UAV systems with RIS-aided communication poses a significant challenge in energy reduction. Therefore, this study investigates a new RIS-aided multi-UAV communication framework for edge computing systems. The system aims to meet the quality-of-service (QoS) for UEs while minimizing the total energy consumption. To optimize the total energy consumption of RIS-aided multi-UAV communication, the impact of communication between multiple UAVs and differences between UE clusters on that system’s performance is also considered. We introduce a Stackelberg game to deal with the communication relationship between multiple UAVs and design a K-means-based clustering algorithm to segment UEs periodically. A model-free deep reinforcement learning algorithm grounded in maximum entropy is proposed to jointly optimize UAV trajectory design, phase shift control, and power allocation to reduce energy consumption further. Experimental results indicate that the system proposed performs favorably concerning both energy consumption and throughput.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"21142-21156"},"PeriodicalIF":7.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753855","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}

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY IEEE 智能交通系统学会

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-05 DOI: 10.1109/TITS.2024.3461528

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IEEE Intelligent Transportation Systems Society Information 电气和电子工程师学会智能交通系统协会信息

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-05 DOI: 10.1109/TITS.2024.3480817

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IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-05 DOI: 10.1109/TITS.2024.3480168

Simona Sacone

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New Spatial Analysis and Hybrid Heuristics Enhance Truck Freight Tonnage Estimation Based on Weigh-in-Motion Data 新的空间分析和混合启发式方法增强了基于动态称重数据的卡车货运吨位估算能力

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-11-01 DOI: 10.1109/TITS.2024.3453268

Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar

{"title":"New Spatial Analysis and Hybrid Heuristics Enhance Truck Freight Tonnage Estimation Based on Weigh-in-Motion Data","authors":"Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar","doi":"10.1109/TITS.2024.3453268","DOIUrl":"https://doi.org/10.1109/TITS.2024.3453268","url":null,"abstract":"This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19581-19591"},"PeriodicalIF":7.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736232","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}

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