IEEE Transactions on Intelligent Transportation Systems最新文献

Exploratory Driving Performance and Car-Following Modeling for Autonomous Shuttles Based on Field Data 基于现场数据的自动驾驶穿梭车探索性驾驶性能及车辆跟随建模

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-09 DOI: 10.1109/TITS.2025.3552506

Renan Favero;Lily Elefteriadou

{"title":"Exploratory Driving Performance and Car-Following Modeling for Autonomous Shuttles Based on Field Data","authors":"Renan Favero;Lily Elefteriadou","doi":"10.1109/TITS.2025.3552506","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552506","url":null,"abstract":"Autonomous shuttles (AS) operate in several cities and have shown potential to improve the public transport network. However, there is no car-following model that is based on field data and allows decision-makers (planners, and traffic engineers) to assess and plan for AS operations. To fill this gap, this study collected field data from AS operations, analyzed their driving performance, and suggested changes in the AS trajectory model to improve passenger comfort. A sample was collected with over 4,000 seconds of data of AS following a conventional car (human driver). The sample contained GPS positions from both AS and conventional vehicles. Latitude and longitude coordinates were used to calculate the speed, acceleration, and jerk of the leader and follower. The data analyses indicated that AS has higher jerk values that may impact the passengers’ comfort. Several existing models were evaluated, and the researchers concluded that the calibrated ACC model resulted in lower errors for AS spacing and speed. The results of the calibration indicate that the AS exhibits lower peak acceleration and higher deceleration than those found in calibrated parameters of autonomous vehicle models from other studies.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6042-6055"},"PeriodicalIF":7.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908424","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

Improved Virtual Vehicles Design for On-Ramp Cooperative Merging 入口匝道协同归并的改进虚拟车辆设计

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-08 DOI: 10.1109/TITS.2025.3554556

Wang Shihui;Zhao Min;Sun Dihua;Wang Liuping

{"title":"Improved Virtual Vehicles Design for On-Ramp Cooperative Merging","authors":"Wang Shihui;Zhao Min;Sun Dihua;Wang Liuping","doi":"10.1109/TITS.2025.3554556","DOIUrl":"https://doi.org/10.1109/TITS.2025.3554556","url":null,"abstract":"Virtual vehicle design has received a lot of attention recently and is commonly used to assist connected and autonomous vehicles (CAVs) in achieving cooperative merging. However, very few virtual vehicles have been designed to assist connected and human-driven vehicles (CHVs) in achieving cooperative control. This paper proposes an improved virtual vehicle design methodology that extends the application of the virtual vehicle concept to CHVs by considering driver compliance to ensure that CHVs and CAVs achieve cooperative merging. This method mainly includes two parts: the computation of vehicle cooperative state based on car-following model and the redesign of virtual vehicles. This paper also analyzes the stability of the mixed platoon composed of CHV and CAV, and obtains the conditions for the string stability of the mixed platoon. Simulation experiments demonstrate that the proposed method can overcome the problems caused by driver compliance in the collaborative process of the on-ramp area. Additionally, the proposed method has the advantages in reducing fuel consumptions and improving the comfort of drivers and passengers. The experiments based on SUMO platform show that the method can reduce traffic density and increase average speed, and that the improvement is more significant at lower CAV penetration rates. It will meet the real-time requirements of daily traffic by analyzing the computation time.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"5872-5887"},"PeriodicalIF":7.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913533","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

DAG Blockchain-Assisted Asynchronous Federated Mutual Learning for Autonomous Driving DAG区块链辅助的自动驾驶异步联邦相互学习

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-07 DOI: 10.1109/TITS.2025.3552749

Yuhang Wu;Xiaoge Huang;Bin Cao;Chengchao Liang;Qianbin Chen

{"title":"DAG Blockchain-Assisted Asynchronous Federated Mutual Learning for Autonomous Driving","authors":"Yuhang Wu;Xiaoge Huang;Bin Cao;Chengchao Liang;Qianbin Chen","doi":"10.1109/TITS.2025.3552749","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552749","url":null,"abstract":"Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6263-6275"},"PeriodicalIF":7.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908403","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

D3QN-Based IAB Resource Allocation and Tethered UAV Positioning for IoT Networks 基于d3qn的物联网IAB资源分配与系留无人机定位

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-07 DOI: 10.1109/TITS.2025.3554538

Yerin Lee;Heejung Yu;Howon Lee;Mohamed-Slim Alouini

{"title":"D3QN-Based IAB Resource Allocation and Tethered UAV Positioning for IoT Networks","authors":"Yerin Lee;Heejung Yu;Howon Lee;Mohamed-Slim Alouini","doi":"10.1109/TITS.2025.3554538","DOIUrl":"https://doi.org/10.1109/TITS.2025.3554538","url":null,"abstract":"The use of tethered uncrewed aerial vehicles (TUAVs) is promising for addressing the energy-constraint problems associated with battery-powered aerial vehicles. In addition, integrated access and backhaul (IAB) technology allows the simultaneous exploitation of the same frequency band for both access and backhaul links, thus increasing resource utilization efficiency in air-ground integrated Internet of Things (IoT) networks. However, the joint optimization of TUAV deployment and IAB bandwidth allocation is an extremely complicated problem, particularly when considering the dynamic characteristics of TUAV-aided IAB network environments. Therefore, we herein propose a distributed double deep Q-network (D3QN)-based optimal resource allocation and a TUAV deployment algorithm to maximize the network-wide sum rate. By performing extensive simulations, it is shown that the proposed algorithm significantly improves the network-wide sum rate compared with several benchmark algorithms, such as the reward-optimal, random action, fixed channel allocation, fixed transmit power allocation, fixed TUAV positioning, distributed Q-learning, distributed DQN, and centralized DDQN algorithms.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6276-6287"},"PeriodicalIF":7.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908381","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

The Permissioned Blockchain-Based Quantum-Inspired Edge Intelligence Approach for the Services of Future Internet of Vehicles 面向未来车联网服务的基于许可区块链的量子边缘智能方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-03 DOI: 10.1109/TITS.2025.3553403

Dajun Zhang;Wei Shi;Marc St-Hilaire

{"title":"The Permissioned Blockchain-Based Quantum-Inspired Edge Intelligence Approach for the Services of Future Internet of Vehicles","authors":"Dajun Zhang;Wei Shi;Marc St-Hilaire","doi":"10.1109/TITS.2025.3553403","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553403","url":null,"abstract":"The Internet of Vehicles (IoV) has become a key pillar in the future network system. However, intensive computing and task offloading required vehicles to compete for communication and computing resources, seriously affecting the systems time cost, robustness, and security. This paper focuses on solving resource management problems in the presence of interconnected multi-vehicles using shared information. We model this problem using a time-varying Markov decision process, addressing the challenges in task offloading for vehicles. The innovation lies in addressing different offloading scenarios, including vehicle-to-vehicle, vehicle-to-roadside unit (RSU) vehicle-to-multi-access edge computing (MAEC) server offloading, and vehicle-to-base station (BS). We propose a Quantum-inspired Dueling Deep Q-learning (QDDQL) algorithm to develop an Edge Intelligent (EI) offloading strategy. This method allows vehicles’ task offload to become an automated step based on network conditions and user status. The MAEC server offers computing offloading services, while the base station can submit offloading tasks to a cloud blockchain system. This innovative approach balances communication resource utilization, computational resource utilization, and transmission delay. Blockchain technology ensures transparency and security in resource allocation strategies, preventing edge nodes from making wrong decisions using consensus mechanism, and thereby improving the accuracy, timeliness, and security of resource allocation. Simulation results show that compared with existing methods, the proposed solution can significantly improve resource utilization, adaptability, and system scalability, and effectively address the defects of traditional methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6171-6185"},"PeriodicalIF":7.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908406","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

SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap SkyLoc：跨模态全球定位与天空看鱼眼相机和OpenStreetMap

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-03 DOI: 10.1109/TITS.2025.3550941

Weixin Ma;Shoudong Huang;Yuxiang Sun

{"title":"SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap","authors":"Weixin Ma;Shoudong Huang;Yuxiang Sun","doi":"10.1109/TITS.2025.3550941","DOIUrl":"https://doi.org/10.1109/TITS.2025.3550941","url":null,"abstract":"Global localization can estimate geo-referenced locations (e.g., longitude and latitude), which is a fundamental capability for autonomous vehicles. Most existing solutions rely on the Global Navigation Satellite Systems (GNSS). Their accuracy could be degraded by the multi-path effects or occlusions of GNSS signals in urban environments. Some GNSS-free methods could achieve global localization by comparing the current on-line sensory data with pre-built databases/maps. However, they require tedious human efforts to drive a vehicle to collect and maintain the databases/maps. Moreover, most of these methods use front-looking cameras or LiDARs, so the captured data could be easily contaminated by dynamic objects (e.g., moving vehicles and pedestrians). To provide a solution to these problems, this paper proposes a novel global localization method by comparing an image from a sky-looking fish-eye camera with the publicly available OpenStreetMap (OSM), and using particle filter to achieve real-time metric localization in dynamic traffic environments. To evaluate our method, we extend a public dataset with OSM data, which are retrieved through the given geo-referenced location information. Experimental results demonstrate the effectiveness and efficiency of our method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"5832-5842"},"PeriodicalIF":7.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913496","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

Design and Control of Personalized Steering Feel for Steer-by-Wire Systems 线控转向系统个性化转向感觉的设计与控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-02 DOI: 10.1109/TITS.2025.3554276

Qingya Zhou;Liang Liu;Zhaoping Xu;Xianhui Wang

{"title":"Design and Control of Personalized Steering Feel for Steer-by-Wire Systems","authors":"Qingya Zhou;Liang Liu;Zhaoping Xu;Xianhui Wang","doi":"10.1109/TITS.2025.3554276","DOIUrl":"https://doi.org/10.1109/TITS.2025.3554276","url":null,"abstract":"The realization of personalized steering feel plays a crucial role in prompting the widespread adoption and driver acceptance of autonomous vehicles (AVs). Since human drivers have distinctly different steering feel preferences and requirements, the personalized matching is essential for ensuring safety and comfort of human-vehicle collaboration. Therefore, this paper proposed a novel personalized steering feel design method based on the driver’s steering characteristics, utilizing steer-by-wire (SBW) vehicle as the platform. Specifically, the personalized steering feel was composed of conventional steering torque, customizable module and mechanical compensation. The conventional steering torque was calculated by a tire model. Then, the concept of road sense style was the first introduced as the basis for the design of the customizable module. Furthermore, a complete road sense style recognition system was put forward from the perspective of semi-supervised learning. The driver’s steering characteristics were collected and classified by k-means algorithm, and a generic road sense style recognition model was built and optimized through support vector machine with crow search (CS-SVM) method. The recognition results were integrated into the design of the personalized steering feel. In addition, a steering controller adopting sliding mode control (SMC) was designed to facilitate stable and rational steering maneuvers by drivers. Finally, driving experiments were performed. The results show that the designed steering feel can rapidly adapt to diverse drivers’ preferences, offering a more satisfying driving experience and improving overall safety.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6288-6303"},"PeriodicalIF":7.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908369","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

Vision-Based Driving Decision Making Using Multi-Action Deep Q Network 基于视觉的多动作深度Q网络驾驶决策

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-02 DOI: 10.1109/TITS.2025.3552993

Sheng Yuan;Yaochen Li;Kai Zhao;Li Zhu;Jiaxin Guo;Xinnan Ma;Yuncheng Xu

{"title":"Vision-Based Driving Decision Making Using Multi-Action Deep Q Network","authors":"Sheng Yuan;Yaochen Li;Kai Zhao;Li Zhu;Jiaxin Guo;Xinnan Ma;Yuncheng Xu","doi":"10.1109/TITS.2025.3552993","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552993","url":null,"abstract":"The performance improvement of perception algorithms and integrated validation of driving decision making remains challenging in the fields of computer vision and intelligent transportation systems. In this paper, we propose a novel vision-based framework for driving decision making, which is composed of three stages: object perception, lane line perception and driving decision making. For the object perception stage, an improved object perception model named CenterNet-ARA is developed, composing of a new adversarial training method, a receptive field enhancement module and an adaptive sample allocation equalization strategy to fuse multi-scale feature maps. For the lane line perception stage, a lane line perception method named Lite-MobileTR is proposed, which contains an improved Lite-MobileNetV3 encoder and an improved lite-transformer decoder. Moreover, a noise removal task is incorporated to alleviate the problem of slow convergence speed caused by Hungarian loss function. For the driving decision making stage, a new Multi-Action DQN is proposed utilizing a vehicle curriculum learning strategy and a curiosity exploration strategy to alleviate the problem of random exploration in the learning process. The proposed framework is evaluated on the Tusimple, CULane, TSD-max, and KITTI datasets. Finally, an integration verification is performed in Carla simulator to validate the driving decision making process. The experimental results well demonstrate the effectiveness of the proposed framework.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"5816-5831"},"PeriodicalIF":7.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913590","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

Pedestrian–Vehicle Interaction Analysis Based on Concept of Dynamic Straight-Right Lane at Signalized Intersection 基于信号交叉口动态直右车道概念的人-车交互分析

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-02 DOI: 10.1109/TITS.2025.3548724

Shidong Liang;Tianyu Zhao;Jing Zhao

{"title":"Pedestrian–Vehicle Interaction Analysis Based on Concept of Dynamic Straight-Right Lane at Signalized Intersection","authors":"Shidong Liang;Tianyu Zhao;Jing Zhao","doi":"10.1109/TITS.2025.3548724","DOIUrl":"https://doi.org/10.1109/TITS.2025.3548724","url":null,"abstract":"Straight-right mixed lanes are common in urban signalized intersections. Right-turning vehicles during green light will conflict with pedestrians, increasing the risk to pedestrian safety. In addition, right-turning vehicles cannot pass the intersection during the red light, which seriously affects the saturation rate of the traffic flow. To solve the above problems, this paper proposes dynamic straight-right lane (DSRL) design scheme that is able to separate straight-through and right-turning vehicles in time and space. Based on the driving characteristics of right-turning vehicles, the operating rules for DSRL have been established. Combined with the intersection design, DSRL control strategy is proposed under the linkage of intersection traffic light and pre-signals. By studying the pedestrian crossing characteristics, the conflict between right-turning vehicles entering the intersection and crossing pedestrians is analyzed under the DSRL. Vehicle delays and traffic capacity are quantified under the DSRL on the basis of vehicle operating rules. The vehicle delay model is tested using SUMO simulation software to prove the validity of the model proposed in this paper. Finally, the simulation environment based on real intersections was set up using MATLAB and sensitivity analyses of the main impact parameters were carried out. It has been experimentally demonstrated that the DSRL not only effectively reduces the delay of both straight-through and right-turning vehicles, but also improves the actual capacity of the lane. In addition, the DSRL reduces conflicts between right-turning vehicles and pedestrians crossing the street. The experimental results can effectively prove that the DSRL can improve the traffic efficiency of vehicle and ensure the safety of pedestrians.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6017-6041"},"PeriodicalIF":7.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913306","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

Deep Reinforcement Learning-Based Computation Computational Offloading for Space–Air–Ground Integrated Vehicle Networks 基于深度强化学习的天空地一体化车辆网络计算卸载

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-02 DOI: 10.1109/TITS.2025.3551636

Wenxuan Xie;Chen Chen;Ying Ju;Jun Shen;Qingqi Pei;Houbing Song

{"title":"Deep Reinforcement Learning-Based Computation Computational Offloading for Space–Air–Ground Integrated Vehicle Networks","authors":"Wenxuan Xie;Chen Chen;Ying Ju;Jun Shen;Qingqi Pei;Houbing Song","doi":"10.1109/TITS.2025.3551636","DOIUrl":"https://doi.org/10.1109/TITS.2025.3551636","url":null,"abstract":"In remote or disaster areas, where terrestrial networks are difficult to cover and Terrestrial Edge Computing (TEC) infrastructures are unavailable, solving the computation computational offloading for Internet of Vehicles (IoV) scenarios is challenging. Current terrestrial networks have high data rates, great connectivity, and low delay, but global coverage is limited. Space–Air–Ground Integrated Networks (SAGIN) can improve the coverage limitations of terrestrial networks and enhance disaster resistance. However, the rising complexity and heterogeneity of networks make it difficult to find a robust and intelligent computational offload strategy. Therefore, joint scheduling of space, air, and ground resources is needed to meet the growing demand for services. In light of this, we propose an integrated network framework for Space-Air Auxiliary Vehicle Computation (SA-AVC) and build a system model to support various IoV services in remote areas. Our model aims to maximize delay and fair utility and increase the utilization of satellites and Autonomous aerial vehicles (AAVs). To this end, we propose a Deep Reinforcement Learning algorithm to achieve real-time computational computational offloading decisions. We utilize the Rank-based Prioritization method in Prioritized Experience Replay (PER) to optimize our algorithm. We designed simulation experiments for validation and the results show that our proposed algorithm reduces the average system delay by 17.84%, 58.09%, and 58.32%, and the average variance of the task completion delay will be reduced by 29.41%, 48.74%, and 49.58% compared to the Deep Q Network (DQN), Q-learning and RandomChoose algorithms.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"5804-5815"},"PeriodicalIF":7.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913497","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