Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210021
Wenfeng Lin;Xiaowei Hu;Jian Wang
Reinforcement learning (RL) can free automated vehicles (AVs) from the car-following constraints and provide more possible explorations for mixed behavior. This study uses deep RL as AVs' longitudinal control and designs a multi-level objectives framework for AVs' trajectory decision-making based on multi-agent DRL. The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control. The simulation results demonstrate the convergence of the proposed framework in complex scenarios. When prioritizing throughputs as the primary objective and emissions as the secondary objective, both indicators exhibit a linear growth pattern with increasing market penetration rate (MPR). Compared with MPR is 0%, the throughputs can be increased by 69.2% when MPR is 100%. Compared with linear adaptive cruise control (LACC) under the same MPR, the emissions can also be reduced by up to 78.8%. Under the control of the fixed throughputs, compared with LACC, the emission benefits grow nearly linearly as MPR increases, it can reach 79.4% at 80% MPR. This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency. The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.
{"title":"Multi-Level Objective Control of AVs at a Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach","authors":"Wenfeng Lin;Xiaowei Hu;Jian Wang","doi":"10.26599/JICV.2023.9210021","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210021","url":null,"abstract":"Reinforcement learning (RL) can free automated vehicles (AVs) from the car-following constraints and provide more possible explorations for mixed behavior. This study uses deep RL as AVs' longitudinal control and designs a multi-level objectives framework for AVs' trajectory decision-making based on multi-agent DRL. The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control. The simulation results demonstrate the convergence of the proposed framework in complex scenarios. When prioritizing throughputs as the primary objective and emissions as the secondary objective, both indicators exhibit a linear growth pattern with increasing market penetration rate (MPR). Compared with MPR is 0%, the throughputs can be increased by 69.2% when MPR is 100%. Compared with linear adaptive cruise control (LACC) under the same MPR, the emissions can also be reduced by up to 78.8%. Under the control of the fixed throughputs, compared with LACC, the emission benefits grow nearly linearly as MPR increases, it can reach 79.4% at 80% MPR. This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency. The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"250-263"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504514","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}
Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210023
Lan Yang;Jiaqi Yuan;Xiangmo Zhao;Shan Fang;Zeyu He;Jiahao Zhan;Zhiqiang Hu;Xia Li
With the increasing level of automation of autonomous vehicles, it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market. Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage. Therefore, scenario-based autonomous vehicle simulation testing has emerged. Many scenarios form the basis of simulation testing. Generating additional scenarios from an existing scenario library is a significant problem. Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example, based on an autoencoder and a generative adversarial network (GAN), a method that combines Transformer to capture the features of a long-time series, called SceGAN, is proposed to model and generate scenarios of autonomous vehicles on highways. An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage. Experiments showed that compared with TimeGAN and AEGAN, SceGAN is superior in data fidelity and availability, and their similarity increased by 27.22% and 21.39%, respectively. The coverage increased from 79.84% to 93.98% as generated scenarios increased from 2,547 to 50,000, indicating that the proposed method has a strong generalization capability for generating multiple trajectories, providing a basis for generating test scenarios and promoting autonomous vehicle testing.
{"title":"SceGAN: A Method for Generating Autonomous Vehicle Cut-In Scenarios on Highways Based on Deep Learning","authors":"Lan Yang;Jiaqi Yuan;Xiangmo Zhao;Shan Fang;Zeyu He;Jiahao Zhan;Zhiqiang Hu;Xia Li","doi":"10.26599/JICV.2023.9210023","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210023","url":null,"abstract":"With the increasing level of automation of autonomous vehicles, it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market. Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage. Therefore, scenario-based autonomous vehicle simulation testing has emerged. Many scenarios form the basis of simulation testing. Generating additional scenarios from an existing scenario library is a significant problem. Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example, based on an autoencoder and a generative adversarial network (GAN), a method that combines Transformer to capture the features of a long-time series, called SceGAN, is proposed to model and generate scenarios of autonomous vehicles on highways. An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage. Experiments showed that compared with TimeGAN and AEGAN, SceGAN is superior in data fidelity and availability, and their similarity increased by 27.22% and 21.39%, respectively. The coverage increased from 79.84% to 93.98% as generated scenarios increased from 2,547 to 50,000, indicating that the proposed method has a strong generalization capability for generating multiple trajectories, providing a basis for generating test scenarios and promoting autonomous vehicle testing.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"264-274"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504506","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210022
Lan Yang;Zeyu He;Xiangmo Zhao;Shan Fang;Jiaqi Yuan;Yixu He;Shijie Li;Songyan Liu
Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems. To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios, we propose an end-to-end traffic light status recognition method, ResNeSt50-CBAM-DINO (RC-DINO). First, we performed data cleaning on the Tsinghua-Tencent traffic lights (TTTL) and fused it with the Shanghai Jiao Tong University's traffic light dataset (S2TLD) to form a Chinese urban traffic light dataset (CUTLD). Second, we combined residual network with split-attention module-50 (ResNeSt50) and the convolutional block attention module (CBAM) to extract more significant traffic light features. Finally, the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD. The experimental results show that, compared to the original DINO, RC-DINO improved the average precision (AP), AP at intersection over union (IOU) = 0.5 (AP 50 ), AP for small objects (APs), average recall (AR), and balanced F score (F1-Score) by 3.1 %, 1.6%, 3.4%, 0.9%, and 0.9%, respectively, and had a certain capability to recognize the partially covered traffic light status. The above results indicate that the proposed RC-DINO improved recognition performance and robustness, making it more suitable for traffic light status recognition tasks.
{"title":"A Deep Learning Method for Traffic Light Status Recognition","authors":"Lan Yang;Zeyu He;Xiangmo Zhao;Shan Fang;Jiaqi Yuan;Yixu He;Shijie Li;Songyan Liu","doi":"10.26599/JICV.2023.9210022","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210022","url":null,"abstract":"Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems. To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios, we propose an end-to-end traffic light status recognition method, ResNeSt50-CBAM-DINO (RC-DINO). First, we performed data cleaning on the Tsinghua-Tencent traffic lights (TTTL) and fused it with the Shanghai Jiao Tong University's traffic light dataset (S2TLD) to form a Chinese urban traffic light dataset (CUTLD). Second, we combined residual network with split-attention module-50 (ResNeSt50) and the convolutional block attention module (CBAM) to extract more significant traffic light features. Finally, the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD. The experimental results show that, compared to the original DINO, RC-DINO improved the average precision (AP), AP at intersection over union (IOU) = 0.5 (AP\u0000<inf>50</inf>\u0000), AP for small objects (APs), average recall (AR), and balanced F score (F1-Score) by 3.1 %, 1.6%, 3.4%, 0.9%, and 0.9%, respectively, and had a certain capability to recognize the partially covered traffic light status. The above results indicate that the proposed RC-DINO improved recognition performance and robustness, making it more suitable for traffic light status recognition tasks.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"173-182"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081196","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210016
Lipeng Cao;Yugong Luo;Yongsheng Wang;Jian Chen;Yansong He
On highways, vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles. To ensure their safety, predicting the sideslip trajectories of such vehicles is crucial. However, the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction. Hence, this study uses a physical model-based approach to predict vehicle sideslip trajectories. Nevertheless, the traditional physical model-based method relies on constant input assumption, making its long-term prediction accuracy poor. To address this challenge, this study presents the time-series analysis and interacting multiple model-based (IMM) sideslip trajectory prediction (TSIMMSTP) method, which encompasses time-series analysis and multi-physical model fusion, for the prediction of vehicle sideslip trajectories. Firstly, we use the proposed adaptive quadratic exponential smoothing method with damping (AQESD) in the time-series analysis module to predict the input state sequence required by kinematic models. Then, we employ an IMM approach to fuse the prediction results of various physical models. The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories. The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios, and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.
{"title":"Vehicle Sideslip Trajectory Prediction Based on Time-Series Analysis and Multi-Physical Model Fusion","authors":"Lipeng Cao;Yugong Luo;Yongsheng Wang;Jian Chen;Yansong He","doi":"10.26599/JICV.2023.9210016","DOIUrl":"10.26599/JICV.2023.9210016","url":null,"abstract":"On highways, vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles. To ensure their safety, predicting the sideslip trajectories of such vehicles is crucial. However, the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction. Hence, this study uses a physical model-based approach to predict vehicle sideslip trajectories. Nevertheless, the traditional physical model-based method relies on constant input assumption, making its long-term prediction accuracy poor. To address this challenge, this study presents the time-series analysis and interacting multiple model-based (IMM) sideslip trajectory prediction (TSIMMSTP) method, which encompasses time-series analysis and multi-physical model fusion, for the prediction of vehicle sideslip trajectories. Firstly, we use the proposed adaptive quadratic exponential smoothing method with damping (AQESD) in the time-series analysis module to predict the input state sequence required by kinematic models. Then, we employ an IMM approach to fuse the prediction results of various physical models. The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories. The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios, and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"161-172"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10310059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135503821","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210009
Benjamin Acar;Marius Sterling
The application of machine learning techniques, particularly in the context of autonomous driving solutions, has grown exponentially in recent years. As such, the collection of high-quality datasets has become a prerequisite for training new models. However, concerns about privacy and data usage have led to a growing demand for decentralized methods that can be learned without the need for pre-collected data. Federated learning (FL) offers a potential solution to this problem by enabling individual clients to contribute to the learning process by sending model updates rather than training data. While Federated Learning has proven successful in many cases, new challenges have emerged, especially in terms of network availability during training. Since a global instance is responsible for collecting updates from local clients, there is a risk of network downtime if the global server fails. In this study, we propose a novel and crucial concept that addresses this issue by adding redundancy to our network. Rather than deploying a single global model, we deploy a multitude of global models and utilize consensus algorithms to synchronize and keep these replicas updated. By utilizing these replicas, even if the global instance fails, the network remains available. As a result, our solution enables the development of reliable Federated Learning systems, particularly in system architectures suitable for infrastructure-enhanced autonomous driving. Consequently, our findings enable the more effective realization of use cases in the context of cooperative, connected, and automated mobility.
{"title":"Ensuring Federated Learning Reliability for Infrastructure-Enhanced Autonomous Driving","authors":"Benjamin Acar;Marius Sterling","doi":"10.26599/JICV.2023.9210009","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210009","url":null,"abstract":"The application of machine learning techniques, particularly in the context of autonomous driving solutions, has grown exponentially in recent years. As such, the collection of high-quality datasets has become a prerequisite for training new models. However, concerns about privacy and data usage have led to a growing demand for decentralized methods that can be learned without the need for pre-collected data. Federated learning (FL) offers a potential solution to this problem by enabling individual clients to contribute to the learning process by sending model updates rather than training data. While Federated Learning has proven successful in many cases, new challenges have emerged, especially in terms of network availability during training. Since a global instance is responsible for collecting updates from local clients, there is a risk of network downtime if the global server fails. In this study, we propose a novel and crucial concept that addresses this issue by adding redundancy to our network. Rather than deploying a single global model, we deploy a multitude of global models and utilize consensus algorithms to synchronize and keep these replicas updated. By utilizing these replicas, even if the global instance fails, the network remains available. As a result, our solution enables the development of reliable Federated Learning systems, particularly in system architectures suitable for infrastructure-enhanced autonomous driving. Consequently, our findings enable the more effective realization of use cases in the context of cooperative, connected, and automated mobility.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"125-135"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081239","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210024
Kunjun Wang;Ye Xiao;Yixu He
Driven by technological innovation and digital evolution, the current automotive industry is standing at the cusp of a transformative era (Liu et al., 2023). As urban centers continue to expand and intensify the demands on transportation networks, the need for solutions to alleviate congestion, boost traffic efficiency, and enhance road safety becomes increasingly urgent. On this occasion, intelligent and connected vehicles, integrating vehicles, infrastructure, and cloud computing, promise a smarter mode of passenger transportation and pave the way for a more interconnected and responsive urban transit ecosystem (Cao et al., 2023). Therefore, traditional passenger buses are on the verge of significant transformation in terms of their functional technologies and operational models. This will bring about a host of benefits such as higher efficiency, better passenger experiences, and safer road environments. This paper provides a comprehensive outlook on intelligent and connected passenger buses (ICPBs), delving into the integrated vehicle-road-cloud platform and highlighting the key technologies that will shape the future bus system. As illustrated in Fig. 1, it showcases the key perspectives on the future of ICPBs.
{"title":"Charting the Future: Intelligent and Connected Vehicles Reshaping the Bus System","authors":"Kunjun Wang;Ye Xiao;Yixu He","doi":"10.26599/JICV.2023.9210024","DOIUrl":"10.26599/JICV.2023.9210024","url":null,"abstract":"Driven by technological innovation and digital evolution, the current automotive industry is standing at the cusp of a transformative era (Liu et al., 2023). As urban centers continue to expand and intensify the demands on transportation networks, the need for solutions to alleviate congestion, boost traffic efficiency, and enhance road safety becomes increasingly urgent. On this occasion, intelligent and connected vehicles, integrating vehicles, infrastructure, and cloud computing, promise a smarter mode of passenger transportation and pave the way for a more interconnected and responsive urban transit ecosystem (Cao et al., 2023). Therefore, traditional passenger buses are on the verge of significant transformation in terms of their functional technologies and operational models. This will bring about a host of benefits such as higher efficiency, better passenger experiences, and safer road environments. This paper provides a comprehensive outlook on intelligent and connected passenger buses (ICPBs), delving into the integrated vehicle-road-cloud platform and highlighting the key technologies that will shape the future bus system. As illustrated in Fig. 1, it showcases the key perspectives on the future of ICPBs.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"113-115"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10310061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135504037","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210020
Hongyi Lin;Yiping Yan;Qixiu Cheng
Over the past decade, the rapid evolution of artificial intelligence (AI) has revolutionized various sectors, including transportation. This discussion explores the transformative potential of AI in enhancing transportation electrification, focusing on its role in battery management, vehicle speed regulation, and personalized route recommendations for Autonomous Electric Vehicles (AEVs).
{"title":"Future Role of Artificial Intelligence in Advancing Transportation Electrification","authors":"Hongyi Lin;Yiping Yan;Qixiu Cheng","doi":"10.26599/JICV.2023.9210020","DOIUrl":"10.26599/JICV.2023.9210020","url":null,"abstract":"Over the past decade, the rapid evolution of artificial intelligence (AI) has revolutionized various sectors, including transportation. This discussion explores the transformative potential of AI in enhancing transportation electrification, focusing on its role in battery management, vehicle speed regulation, and personalized route recommendations for Autonomous Electric Vehicles (AEVs).","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"183-186"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10310060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135503807","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210013
Junfan Zhuo;Feng Zhu
Platooning has emerged to be one of the most promising applications for connected and automated vehicles (CAVs). However, there is still limited research on the effect of platooning configurations. This study sets out to investigate the effect of CAV platoon configurations at a typical isolated roundabout in a mixed traffic environment. Investigated platoon configurations include maximum platoon size, platoon willingness, and platoon type. Extensive simulation experiments are carried out in simulation of urban mobility (SUMO), considering various traffic conditions, including different penetration rates, traffic flows, and turning percentages. Results show that: (1) increasing the maximum platoon size and platoon willingness generally improves the throughput increment and delay reduction; and (2) heterogeneous platoons outperform homogeneous platoons in all traffic conditions.
{"title":"Evaluation of Platooning Configurations for Connected and Automated Vehicles at an Isolated Roundabout in a Mixed Traffic Environment","authors":"Junfan Zhuo;Feng Zhu","doi":"10.26599/JICV.2023.9210013","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210013","url":null,"abstract":"Platooning has emerged to be one of the most promising applications for connected and automated vehicles (CAVs). However, there is still limited research on the effect of platooning configurations. This study sets out to investigate the effect of CAV platoon configurations at a typical isolated roundabout in a mixed traffic environment. Investigated platoon configurations include maximum platoon size, platoon willingness, and platoon type. Extensive simulation experiments are carried out in simulation of urban mobility (SUMO), considering various traffic conditions, including different penetration rates, traffic flows, and turning percentages. Results show that: (1) increasing the maximum platoon size and platoon willingness generally improves the throughput increment and delay reduction; and (2) heterogeneous platoons outperform homogeneous platoons in all traffic conditions.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"136-148"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081302","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210012
Le Zhang;Yu Han;Jiankun Peng;Yadong Wang
Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits it offers. However, the adoption of battery electric buses is still impeded by their limited flexibility, a constraint that necessitates adjustments to current bus scheduling plans. Consequently, this study aspires to offer a thorough review of articles focused on battery electric bus scheduling. Moreover, we provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain. Despite this extensive review, our findings reveal a paucity of research that takes into account the robustness of electric bus scheduling. Furthermore, we highlight the critical areas of considering diverse charging modes in electric bus scheduling and integrated planning of electric buses, which have not been adequately explored but hold the potential to greatly boost the effectiveness of electric bus systems. Through this synthesis, we hope that readers could acquire a thorough comprehension of the studies in this field and be motivated to address the identified research gaps, thus propelling the progress of transit electrification.
{"title":"Vehicle and Charging Scheduling of Electric Bus Fleets: A Comprehensive Review","authors":"Le Zhang;Yu Han;Jiankun Peng;Yadong Wang","doi":"10.26599/JICV.2023.9210012","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210012","url":null,"abstract":"Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits it offers. However, the adoption of battery electric buses is still impeded by their limited flexibility, a constraint that necessitates adjustments to current bus scheduling plans. Consequently, this study aspires to offer a thorough review of articles focused on battery electric bus scheduling. Moreover, we provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain. Despite this extensive review, our findings reveal a paucity of research that takes into account the robustness of electric bus scheduling. Furthermore, we highlight the critical areas of considering diverse charging modes in electric bus scheduling and integrated planning of electric buses, which have not been adequately explored but hold the potential to greatly boost the effectiveness of electric bus systems. Through this synthesis, we hope that readers could acquire a thorough comprehension of the studies in this field and be motivated to address the identified research gaps, thus propelling the progress of transit electrification.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"116-124"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081200","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}
Pub Date : 2023-09-01DOI: 10.26599/JICV.2023.9210014
Yuhao Wang;Ivan Wang-Hei Ho;Yuhong Wang
The intelligent transportation system (ITS) is committed to ensuring safe and effective next-generation traffic throughout a city. However, such efficient operation on urban traffic networks needs the support of big traffic data, especially Turning Movement Counts (TMC) at intersections. Generally, TMC data are more challenging to collect due to labor cost and accuracy problems. In this paper, we leverage the capabilities of Unmanned Aerial Vehicles (UAV) to collect real-time TMC data in a cost-efficient way. We proposed a real-time TMC data collection framework based on a live video stream. The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection. In addition, a challenging case study was conducted, and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework. Specifically, with a GTX 1650 graphics card, about 10 FPS can be achieved in real-time for the TMC data collection. The overall accuracy is 91.93%, and the best case is over 98% accurate. In the context of miscounting, the major reason is due to ID switching caused by background occlusion. The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.
{"title":"Real-Time Intersection Vehicle Turning Movement Counts from Live UAV Video Stream Using Multiple Object Tracking","authors":"Yuhao Wang;Ivan Wang-Hei Ho;Yuhong Wang","doi":"10.26599/JICV.2023.9210014","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210014","url":null,"abstract":"The intelligent transportation system (ITS) is committed to ensuring safe and effective next-generation traffic throughout a city. However, such efficient operation on urban traffic networks needs the support of big traffic data, especially Turning Movement Counts (TMC) at intersections. Generally, TMC data are more challenging to collect due to labor cost and accuracy problems. In this paper, we leverage the capabilities of Unmanned Aerial Vehicles (UAV) to collect real-time TMC data in a cost-efficient way. We proposed a real-time TMC data collection framework based on a live video stream. The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection. In addition, a challenging case study was conducted, and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework. Specifically, with a GTX 1650 graphics card, about 10 FPS can be achieved in real-time for the TMC data collection. The overall accuracy is 91.93%, and the best case is over 98% accurate. In the context of miscounting, the major reason is due to ID switching caused by background occlusion. The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 3","pages":"149-160"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10339159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139090459","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}
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