Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3523748
Gulshan Kumar;Rahul Saha;Mauro Conti;Joel J. P. C. Rodrigues
The integration of Electric Vehicles (EVs) into the energynet, the network from power generation to EV charging station, presents a symbiotic relationship with potential benefits for sustainable and efficient transportation. However, the existing research has revealed challenges in maintaining an equilibrium between energy supply and demand, often resulting in underutilization or overutilization of energy networks. Blockchain technology has emerged as a promising solution to enhance transparency and secure decentralized energy distribution; however fails to connect the equilibrium in the presence of uncertainty of demand-supply and/or handling information cascading. In this paper, we introduce TAURITE (sTAckelberg eqUilibRium in blockchaIned energyneT with Evs), a novel blockchain-based energynet framework that explicitly leverages the Stackelberg model for energy flow equilibrium within EV interfaces. TAURITE employs Subgame Perfect Nash Equilibrium (SPNE) to address demand uncertainty in dynamic vehicular environments. It also tackles information cascades’ impact on energy distribution, demonstrating its ability to maintain equilibrium even in such scenarios. TAURITE introduces a multi-variate polynomial-based key generation process through the smart contract AVTAL and incorporates Proof-of-Energy-Equilibrium (PoEE) as an energy sector consensus mechanism. Experimental results show that TAURITE significantly improves throughput, latency, and energy efficiency, with an average 30% enhancement in these metrics. Notably, TAURITE ensures 100% allocation stability, even in the presence of information cascades, marking a substantial advancement in sustainable and efficient energy management within the evolving energynet-EV ecosystem.
{"title":"TAURITE: Stackelberg Equilibrium in Blockchained Energynet Through Electric Vehicles","authors":"Gulshan Kumar;Rahul Saha;Mauro Conti;Joel J. P. C. Rodrigues","doi":"10.1109/TITS.2024.3523748","DOIUrl":"https://doi.org/10.1109/TITS.2024.3523748","url":null,"abstract":"The integration of Electric Vehicles (EVs) into the energynet, the network from power generation to EV charging station, presents a symbiotic relationship with potential benefits for sustainable and efficient transportation. However, the existing research has revealed challenges in maintaining an equilibrium between energy supply and demand, often resulting in underutilization or overutilization of energy networks. Blockchain technology has emerged as a promising solution to enhance transparency and secure decentralized energy distribution; however fails to connect the equilibrium in the presence of uncertainty of demand-supply and/or handling information cascading. In this paper, we introduce TAURITE (sTAckelberg eqUilibRium in blockchaIned energyneT with Evs), a novel blockchain-based energynet framework that explicitly leverages the Stackelberg model for energy flow equilibrium within EV interfaces. TAURITE employs Subgame Perfect Nash Equilibrium (SPNE) to address demand uncertainty in dynamic vehicular environments. It also tackles information cascades’ impact on energy distribution, demonstrating its ability to maintain equilibrium even in such scenarios. TAURITE introduces a multi-variate polynomial-based key generation process through the smart contract AVTAL and incorporates Proof-of-Energy-Equilibrium (PoEE) as an energy sector consensus mechanism. Experimental results show that TAURITE significantly improves throughput, latency, and energy efficiency, with an average 30% enhancement in these metrics. Notably, TAURITE ensures 100% allocation stability, even in the presence of information cascades, marking a substantial advancement in sustainable and efficient energy management within the evolving energynet-EV ecosystem.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"4122-4139"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535436","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}
Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3522195
Yinjie Huang;Fuyuan Wang
Traffic signal light detection poses significant challenges in the intelligent driving sector, with high precision and efficiency being crucial for system safety. Advances in deep learning have led to significant improvements in image object detection. However, existing methods continue to struggle with balancing detection speed and accuracy. We propose a lightweight model for traffic light detection that uses a streamlined backbone network and a Low-GD neck architecture. The model’s backbone employs structured reparameterization and lightweight Vision Transformers, using multi-branch and Feed-Forward Network structures to boost informational richness and positional awareness, respectively. The Neck network utilizes the Low-GD structure to enhance the aggregation and integration of multi-scale features, reducing information loss during cross-layer exchanges. We introduce a data augmentation strategy using Stable Diffusion to expand our traffic light dataset in complex weather conditions like fog, rain, and snow, improving model generalization. Our method excels on the YCTL2024 traffic light dataset, achieving a detection speed of 135 FPS and 98.23% accuracy, with only 1.3M model parameters. Testing on the Bosch Small Traffic Lights Dataset confirms the method’s strong generalization capabilities. This suggests that our proposed method can effectively provide accurate and real-time traffic light detection.
{"title":"D-TLDetector: Advancing Traffic Light Detection With a Lightweight Deep Learning Model","authors":"Yinjie Huang;Fuyuan Wang","doi":"10.1109/TITS.2024.3522195","DOIUrl":"https://doi.org/10.1109/TITS.2024.3522195","url":null,"abstract":"Traffic signal light detection poses significant challenges in the intelligent driving sector, with high precision and efficiency being crucial for system safety. Advances in deep learning have led to significant improvements in image object detection. However, existing methods continue to struggle with balancing detection speed and accuracy. We propose a lightweight model for traffic light detection that uses a streamlined backbone network and a Low-GD neck architecture. The model’s backbone employs structured reparameterization and lightweight Vision Transformers, using multi-branch and Feed-Forward Network structures to boost informational richness and positional awareness, respectively. The Neck network utilizes the Low-GD structure to enhance the aggregation and integration of multi-scale features, reducing information loss during cross-layer exchanges. We introduce a data augmentation strategy using Stable Diffusion to expand our traffic light dataset in complex weather conditions like fog, rain, and snow, improving model generalization. Our method excels on the YCTL2024 traffic light dataset, achieving a detection speed of 135 FPS and 98.23% accuracy, with only 1.3M model parameters. Testing on the Bosch Small Traffic Lights Dataset confirms the method’s strong generalization capabilities. This suggests that our proposed method can effectively provide accurate and real-time traffic light detection.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3917-3933"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535545","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}
Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3522308
Dong-Young Kim;Dong-Kyun Han;Ji-Hoon Jeong;Seong-Whan Lee
Drowsy driving is one of the greatest threats to road safety, which increases the importance of intelligent systems that can monitor driver drowsiness. Electroencephalogram (EEG)–based monitoring systems have gained attention because EEG is known to directly measure brain activities that reflect the mental state of the driver. However, calibration is necessary before using the system because EEG signals vary between and within subjects. Therefore, generalized EEG-based drowsiness estimation has become challenging. In this paper, we propose an EEG-based driver drowsiness classification framework without the need for calibration, which can be generalized to unseen subjects. We augment the features of unseen domains (i.e., subjects) with a Dirichlet mixup between prototypes of source domains to complement other domain knowledge. The parameter $boldsymbol {alpha }$ vector of the Dirichlet distribution adjusts the intensity of the mixup, allowing for diverse enhancement. Furthermore, we utilize an auxiliary batch normalization module for augmented samples to avoid inaccurate estimation by the difference in distribution. The experiments were carried out using two EEG datasets, each measured using different drowsiness indicators, the Karolinska sleepiness scale, and reaction time. In leave-one-subject-out cross-validation, the proposed framework achieved outstanding performance in both datasets, an ${F}1$ -score of 62.69% and 70.33% and an area under the receiver operating characteristic curve (AUROC) of 71.73% and 73.80%, respectively. The experimental results demonstrate the potential for practical applications of brain-computer interfaces without calibration.
{"title":"Calibration-Free Driver Drowsiness Classification With Prototype-Based Multi-Domain Mixup","authors":"Dong-Young Kim;Dong-Kyun Han;Ji-Hoon Jeong;Seong-Whan Lee","doi":"10.1109/TITS.2024.3522308","DOIUrl":"https://doi.org/10.1109/TITS.2024.3522308","url":null,"abstract":"Drowsy driving is one of the greatest threats to road safety, which increases the importance of intelligent systems that can monitor driver drowsiness. Electroencephalogram (EEG)–based monitoring systems have gained attention because EEG is known to directly measure brain activities that reflect the mental state of the driver. However, calibration is necessary before using the system because EEG signals vary between and within subjects. Therefore, generalized EEG-based drowsiness estimation has become challenging. In this paper, we propose an EEG-based driver drowsiness classification framework without the need for calibration, which can be generalized to unseen subjects. We augment the features of unseen domains (i.e., subjects) with a Dirichlet mixup between prototypes of source domains to complement other domain knowledge. The parameter <inline-formula> <tex-math>$boldsymbol {alpha }$ </tex-math></inline-formula> vector of the Dirichlet distribution adjusts the intensity of the mixup, allowing for diverse enhancement. Furthermore, we utilize an auxiliary batch normalization module for augmented samples to avoid inaccurate estimation by the difference in distribution. The experiments were carried out using two EEG datasets, each measured using different drowsiness indicators, the Karolinska sleepiness scale, and reaction time. In leave-one-subject-out cross-validation, the proposed framework achieved outstanding performance in both datasets, an <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-score of 62.69% and 70.33% and an area under the receiver operating characteristic curve (AUROC) of 71.73% and 73.80%, respectively. The experimental results demonstrate the potential for practical applications of brain-computer interfaces without calibration.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"2955-2966"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535422","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}
Telematics devices have transformed driver risk assessment, allowing insurers to tailor premiums based on detailed evaluations of driving habits. However, integrating Advanced Driver Assistance Systems (ADAS) and contextualized geolocation data for predictive improvements remains underexplored due to the recent emergence of these technologies. This article introduces a novel risk assessment methodology that periodically computes weekly insurance premiums by incorporating ADAS risk indicators and contextualized geolocation data. Using a naturalistic dataset from a fleet of 354 commercial drivers over a year, we modeled the relationship between past claims and driving data, and use that to compute weekly premiums that penalize risky driving situations. Risk predictions are modeled through claims frequency using Poisson regression and claims occurrence probability using machine learning models, including XGBoost and TabNet, and interpreted with SHAP. The dataset is divided into weekly profiles containing aggregated driving behavior, ADAS events, and contextual attributes. Results indicate that both modeling approaches show consistent attribute impacts on driver risk. For claims occurrence probability, XGBoost achieved the lowest Log Loss, reducing it from 0.59 to 0.51 with the inclusion of all attributes; for claims frequency, no statistically significant differences were observed when including all attributes. However, adding ADAS and contextual attributes allows for a comprehensive and disaggregated interpretation of the resulting weekly premium. This dynamic pricing can be incorporated into the insurance lifecycle, enabling bespoke risk assessment based on emerging technologies, the driving context, and driver behavior.
{"title":"Predictive Modeling for Driver Insurance Premium Calculation Using Advanced Driver Assistance Systems and Contextual Information","authors":"Leandro Masello;Barry Sheehan;German Castignani;Montserrat Guillen;Finbarr Murphy","doi":"10.1109/TITS.2024.3518572","DOIUrl":"https://doi.org/10.1109/TITS.2024.3518572","url":null,"abstract":"Telematics devices have transformed driver risk assessment, allowing insurers to tailor premiums based on detailed evaluations of driving habits. However, integrating Advanced Driver Assistance Systems (ADAS) and contextualized geolocation data for predictive improvements remains underexplored due to the recent emergence of these technologies. This article introduces a novel risk assessment methodology that periodically computes weekly insurance premiums by incorporating ADAS risk indicators and contextualized geolocation data. Using a naturalistic dataset from a fleet of 354 commercial drivers over a year, we modeled the relationship between past claims and driving data, and use that to compute weekly premiums that penalize risky driving situations. Risk predictions are modeled through claims frequency using Poisson regression and claims occurrence probability using machine learning models, including XGBoost and TabNet, and interpreted with SHAP. The dataset is divided into weekly profiles containing aggregated driving behavior, ADAS events, and contextual attributes. Results indicate that both modeling approaches show consistent attribute impacts on driver risk. For claims occurrence probability, XGBoost achieved the lowest Log Loss, reducing it from 0.59 to 0.51 with the inclusion of all attributes; for claims frequency, no statistically significant differences were observed when including all attributes. However, adding ADAS and contextual attributes allows for a comprehensive and disaggregated interpretation of the resulting weekly premium. This dynamic pricing can be incorporated into the insurance lifecycle, enabling bespoke risk assessment based on emerging technologies, the driving context, and driver behavior.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"2202-2211"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3522567
Yibo Ding;Xiaokui Yue;Wenbo Li;Panxing Huang;Naying Li
A novel adaptive finite-time controller (NAFTC) is proposed for flexible air-breathing hypersonic vehicle (FAHV) with actuator saturations, composing of two controllers designed for velocity and height subsystem respectively. Firstly, an adaptive dynamic inversion control is presented for velocity subsystem. The influence of actuator saturation is solved by an improved auxiliary adaptive law (IAAL). Compared with conventional adaptive law, the IAAL can achieve faster convergent speed of tracking error and weaken dramatical change for control signal effectively. Secondly, an adaptive continuous sliding mode control is designed for height subsystem, in which integral sliding surface is established based on a continuous fast higher-order sliding mode algorithm (CFSMA). Compared with conventional finite-time high-order regulator, CFSMA can drive states to converge faster and adjust respond speed of system conveniently without complicated parameters selection. Meanwhile, IAAL is combined to deal with the influence of elevator saturation. Ultimately, with the aid of NAFTC, FAHV subject to actuator constraints can also achieve faster convergent speed. In addition, NAFTC can realize higher tracking precision and faster respond speed compared with existing conventional adaptive controllers.
{"title":"Novel Finite-Time Controller With Improved Auxiliary Adaptive Law for Hypersonic Vehicle Subject to Actuator Constraints","authors":"Yibo Ding;Xiaokui Yue;Wenbo Li;Panxing Huang;Naying Li","doi":"10.1109/TITS.2024.3522567","DOIUrl":"https://doi.org/10.1109/TITS.2024.3522567","url":null,"abstract":"A novel adaptive finite-time controller (NAFTC) is proposed for flexible air-breathing hypersonic vehicle (FAHV) with actuator saturations, composing of two controllers designed for velocity and height subsystem respectively. Firstly, an adaptive dynamic inversion control is presented for velocity subsystem. The influence of actuator saturation is solved by an improved auxiliary adaptive law (IAAL). Compared with conventional adaptive law, the IAAL can achieve faster convergent speed of tracking error and weaken dramatical change for control signal effectively. Secondly, an adaptive continuous sliding mode control is designed for height subsystem, in which integral sliding surface is established based on a continuous fast higher-order sliding mode algorithm (CFSMA). Compared with conventional finite-time high-order regulator, CFSMA can drive states to converge faster and adjust respond speed of system conveniently without complicated parameters selection. Meanwhile, IAAL is combined to deal with the influence of elevator saturation. Ultimately, with the aid of NAFTC, FAHV subject to actuator constraints can also achieve faster convergent speed. In addition, NAFTC can realize higher tracking precision and faster respond speed compared with existing conventional adaptive controllers.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3402-3416"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535505","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}
Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3519199
Taiyu Zhang;Zhengru Ren
Real-time sea state information plays a pivotal role in guiding decisions-making for various marine operations. Utilizing the wave buoy analogy (WBA) enables a cost-effective approach to estimating the wave spectrum through ship motion responses, providing an almost real-time estimation of the sea state. However, non-uniformly distributed response amplitude operators (RAOs) bring about performance deterioration in specific sea states, potentially leading to erroneous estimations that could misguide decision-making and result in severe consequences. Nevertheless, it is possible to combine multiple estimates in a rational manner to improve the robustness and accuracy of the WBA. In this study, the restricted isometry property is introduced to evaluate WBA performance. An RAO-driven assessment criterion is proposed to ascertain the reliability of estimates based solely on RAO input. Building upon this assessment criterion, we propose a multispectral fusion algorithm to amalgamate multiple estimates obtained from ships with different geometries and headings, ultimately generating a comprehensive fused result. Numerical experiments are described to demonstrate the proposed algorithm’s effectiveness.
{"title":"Restricted Isometry Property in Wave Buoy Analogy and Application to Multispectral Fusion","authors":"Taiyu Zhang;Zhengru Ren","doi":"10.1109/TITS.2024.3519199","DOIUrl":"https://doi.org/10.1109/TITS.2024.3519199","url":null,"abstract":"Real-time sea state information plays a pivotal role in guiding decisions-making for various marine operations. Utilizing the wave buoy analogy (WBA) enables a cost-effective approach to estimating the wave spectrum through ship motion responses, providing an almost real-time estimation of the sea state. However, non-uniformly distributed response amplitude operators (RAOs) bring about performance deterioration in specific sea states, potentially leading to erroneous estimations that could misguide decision-making and result in severe consequences. Nevertheless, it is possible to combine multiple estimates in a rational manner to improve the robustness and accuracy of the WBA. In this study, the restricted isometry property is introduced to evaluate WBA performance. An RAO-driven assessment criterion is proposed to ascertain the reliability of estimates based solely on RAO input. Building upon this assessment criterion, we propose a multispectral fusion algorithm to amalgamate multiple estimates obtained from ships with different geometries and headings, ultimately generating a comprehensive fused result. Numerical experiments are described to demonstrate the proposed algorithm’s effectiveness.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1999-2010"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183984","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}
Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3522002
Can Liu;Fangfang Zheng;Henry X. Liu;Xiaobo Liu
This paper presents a traffic oscillation mitigation-oriented optimal control framework for connected and automated vehicles (CAVs) in a mixed traffic environment where the behavior of human-driven vehicles (HVs) is unknown. The primary objective of this framework is to alleviate traffic oscillations, thereby improving overall traffic flow. To achieve this, we introduce a novel total equilibrium spacing estimation method, incorporating stochastic parameters into a car-following model and quantifying the deviation between the mean and equilibrium spacing. This estimation, integrated with a jam-absorption driving strategy, is embedded into a Model Predictive Control (MPC) model for the objective of mitigating traffic oscillations. The efficacy of the proposed control method is evaluated through two experiments utilizing real vehicle trajectory datasets. The first experiment focuses on a single CAV, exploring the impact of key controller parameters on oscillation mitigation. Results demonstrate the optimal performance of the proposed Oscillation Mitigation-based Model Predictive Control (OM-MPC) model, even with a shorter CAV distance (e.g., 100 m), revealing a positive correlation between CAV distance and suitable preset oscillation duration. The second experiment extends the investigation to multiple stop-and-go shockwaves and varying CAV penetration rates. A comparative analysis of control models, including OM-MPC, regular MPC, and proportional-integral with saturation, is conducted based on velocity mean (VM), road segment congestion index (RI), and vehicle stop times (VST). The findings underscore the effectiveness of the proposed control method in mitigating traffic oscillations and enhancing overall traffic efficiency, establishing it as the optimal choice among the three approaches.
{"title":"Optimizing Mixed Traffic Flow: Longitudinal Control of Connected and Automated Vehicles to Mitigate Traffic Oscillations","authors":"Can Liu;Fangfang Zheng;Henry X. Liu;Xiaobo Liu","doi":"10.1109/TITS.2024.3522002","DOIUrl":"https://doi.org/10.1109/TITS.2024.3522002","url":null,"abstract":"This paper presents a traffic oscillation mitigation-oriented optimal control framework for connected and automated vehicles (CAVs) in a mixed traffic environment where the behavior of human-driven vehicles (HVs) is unknown. The primary objective of this framework is to alleviate traffic oscillations, thereby improving overall traffic flow. To achieve this, we introduce a novel total equilibrium spacing estimation method, incorporating stochastic parameters into a car-following model and quantifying the deviation between the mean and equilibrium spacing. This estimation, integrated with a jam-absorption driving strategy, is embedded into a Model Predictive Control (MPC) model for the objective of mitigating traffic oscillations. The efficacy of the proposed control method is evaluated through two experiments utilizing real vehicle trajectory datasets. The first experiment focuses on a single CAV, exploring the impact of key controller parameters on oscillation mitigation. Results demonstrate the optimal performance of the proposed Oscillation Mitigation-based Model Predictive Control (OM-MPC) model, even with a shorter CAV distance (e.g., 100 m), revealing a positive correlation between CAV distance and suitable preset oscillation duration. The second experiment extends the investigation to multiple stop-and-go shockwaves and varying CAV penetration rates. A comparative analysis of control models, including OM-MPC, regular MPC, and proportional-integral with saturation, is conducted based on velocity mean (VM), road segment congestion index (RI), and vehicle stop times (VST). The findings underscore the effectiveness of the proposed control method in mitigating traffic oscillations and enhancing overall traffic efficiency, establishing it as the optimal choice among the three approaches.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3482-3498"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535588","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}
Pub Date : 2025-01-08DOI: 10.1109/TITS.2024.3522243
Yilong Hui;Xiaoqing Ma;Changle Li;Nan Cheng;Rui Chen;Zhisheng Yin;Tom H. Luan;Guoqiang Mao
Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.
{"title":"Service-Oriented Edge Collaboration: Digital Twin Enabled Edge Collaboration for Composite Services in AVNs","authors":"Yilong Hui;Xiaoqing Ma;Changle Li;Nan Cheng;Rui Chen;Zhisheng Yin;Tom H. Luan;Guoqiang Mao","doi":"10.1109/TITS.2024.3522243","DOIUrl":"https://doi.org/10.1109/TITS.2024.3522243","url":null,"abstract":"Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3147-3160"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535581","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}
For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map construction plays a significant role in accurate estimation of the pose. Although recent advancements in online HD map construction have predominantly investigated on vectorized representation due to its effectiveness, they suffer from computational cost and fixed parametric model, which limit scalability. To alleviate these limitations, we propose a novel HD map learning framework that leverages graph modeling. This framework is designed to learn the construction of diverse geometric shapes, thereby enhancing the scalability of HD map construction. Our approach involves representing the map elements as an instance-level graph by decomposing them into vertices and edges to facilitate accurate and efficient end-to-end vectorized HD map learning. Furthermore, we introduce an association strategy using a Graph Neural Network to efficiently handle the complex geometry of various map elements, while maintaining scalability. Comprehensive experiments on public open dataset show that our proposed network outperforms state-of-the-art model by 1.6 mAP. We further showcase the superior scalability of our approach compared to state-of-the-art methods, achieving a 4.8 mAP improvement in long range configuration. Our code is available at https://github.com/juyebshin/InstaGraM.
{"title":"InstaGraM: Instance-Level Graph Modeling for Vectorized HD Map Learning","authors":"Juyeb Shin;Hyeonjun Jeong;Francois Rameau;Dongsuk Kum","doi":"10.1109/TITS.2024.3518537","DOIUrl":"https://doi.org/10.1109/TITS.2024.3518537","url":null,"abstract":"For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map construction plays a significant role in accurate estimation of the pose. Although recent advancements in online HD map construction have predominantly investigated on vectorized representation due to its effectiveness, they suffer from computational cost and fixed parametric model, which limit scalability. To alleviate these limitations, we propose a novel HD map learning framework that leverages graph modeling. This framework is designed to learn the construction of diverse geometric shapes, thereby enhancing the scalability of HD map construction. Our approach involves representing the map elements as an instance-level graph by decomposing them into vertices and edges to facilitate accurate and efficient end-to-end vectorized HD map learning. Furthermore, we introduce an association strategy using a Graph Neural Network to efficiently handle the complex geometry of various map elements, while maintaining scalability. Comprehensive experiments on public open dataset show that our proposed network outperforms state-of-the-art model by 1.6 mAP. We further showcase the superior scalability of our approach compared to state-of-the-art methods, achieving a 4.8 mAP improvement in long range configuration. Our code is available at <uri>https://github.com/juyebshin/InstaGraM</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1889-1899"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183981","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}
We present a novel approach for addressing computer vision tasks in intelligent transportation systems, with a strong focus on data security during training through federated learning. Our method leverages visual transformers, training multiple models for each image. By calculating and storing visual image features as well as loss values, we propose a novel Shapley value model based on model performance consistency to select the most appropriate models during testing. To enhance security, we introduce an intelligent federated learning strategy, where users are grouped into clusters based on constrastive clustering for creating a global model as well as customized local models. Users receive both global as well as local models, enabling tailored computer vision applications. We evaluated KGVT-ITS (Knowledge Guided Visual Transformers for Intelligent Transportation Systems) on various ITS challenges, including pedestrian detection, abnormal event detection, as well as near-crash detection. The results demonstrate the superiority of KGVT-ITS over baseline solutions, showcasing its effectiveness and robustness in intelligent transportation scenarios. More particularly, KGVT-ITS achieves significant improvements of about 8% against the existing ITS methods.
{"title":"Knowledge Guided Visual Transformers for Intelligent Transportation Systems","authors":"Asma Belhadi;Youcef Djenouri;Ahmed Nabil Belbachir;Tomasz Michalak;Gautam Srivastava","doi":"10.1109/TITS.2024.3520487","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520487","url":null,"abstract":"We present a novel approach for addressing computer vision tasks in intelligent transportation systems, with a strong focus on data security during training through federated learning. Our method leverages visual transformers, training multiple models for each image. By calculating and storing visual image features as well as loss values, we propose a novel Shapley value model based on model performance consistency to select the most appropriate models during testing. To enhance security, we introduce an intelligent federated learning strategy, where users are grouped into clusters based on constrastive clustering for creating a global model as well as customized local models. Users receive both global as well as local models, enabling tailored computer vision applications. We evaluated KGVT-ITS (Knowledge Guided Visual Transformers for Intelligent Transportation Systems) on various ITS challenges, including pedestrian detection, abnormal event detection, as well as near-crash detection. The results demonstrate the superiority of KGVT-ITS over baseline solutions, showcasing its effectiveness and robustness in intelligent transportation scenarios. More particularly, KGVT-ITS achieves significant improvements of about 8% against the existing ITS methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3341-3349"},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535466","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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