The task of making lane change decisions for autonomous vehicles in mixed traffic is intricate and challenging due to the uncertainty of surrounding vehicles. The uncertainty exists in terms of the diverse social driving preferences and unpredictable driving behavior of human drivers. To address these challenges, the decision-making process for changing lanes is represented as an incomplete information game, where the driver characteristics of surrounding vehicles are unknown during the interaction. To eliminate the uncertainty of the driving environment, the concept of driver aggressiveness is proposed to quantify the social driving preferences based on the Risk-Response (R-R) diagram in an explainable manner. Then the predicted trajectory is utilized to calculate the driving risks using Gaussian Mixture Model (GMM) that is trained by the naturalistic driving data in the interactive lane change scenarios extracted from the highD dataset. To make the simulation environment more diverse and realistic, the data-driven motion model social Intelligent Driver Model (SIDM) is constructed based on car-following data obtained from cut-in scenarios in the highD dataset. The simulations are conducted by setting up the environment vehicles equipped with SIDM model with diverse social driving preferences. The findings indicate that the proposed decision-making model can recognize the category of surrounding vehicles, and in realistic interactive driving scenarios, it can produce adaptive and human-like driving decisions.
{"title":"Eliminating Uncertainty of Driver’s Social Preferences for Lane Change Decision-Making in Realistic Simulation Environment","authors":"Zejian Deng;Wen Hu;Chen Sun;Duanfeng Chu;Tao Huang;Wenbo Li;Chao Yu;Mohammad Pirani;Dongpu Cao;Amir Khajepour","doi":"10.1109/TITS.2024.3512784","DOIUrl":"https://doi.org/10.1109/TITS.2024.3512784","url":null,"abstract":"The task of making lane change decisions for autonomous vehicles in mixed traffic is intricate and challenging due to the uncertainty of surrounding vehicles. The uncertainty exists in terms of the diverse social driving preferences and unpredictable driving behavior of human drivers. To address these challenges, the decision-making process for changing lanes is represented as an incomplete information game, where the driver characteristics of surrounding vehicles are unknown during the interaction. To eliminate the uncertainty of the driving environment, the concept of driver aggressiveness is proposed to quantify the social driving preferences based on the Risk-Response (R-R) diagram in an explainable manner. Then the predicted trajectory is utilized to calculate the driving risks using Gaussian Mixture Model (GMM) that is trained by the naturalistic driving data in the interactive lane change scenarios extracted from the highD dataset. To make the simulation environment more diverse and realistic, the data-driven motion model social Intelligent Driver Model (SIDM) is constructed based on car-following data obtained from cut-in scenarios in the highD dataset. The simulations are conducted by setting up the environment vehicles equipped with SIDM model with diverse social driving preferences. The findings indicate that the proposed decision-making model can recognize the category of surrounding vehicles, and in realistic interactive driving scenarios, it can produce adaptive and human-like driving decisions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1583-1597"},"PeriodicalIF":7.9,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184011","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 : 2024-12-18DOI: 10.1109/TITS.2024.3509954
Xiangzheng Zhou;Xiaobo Chen;Jian Yang
Trajectory prediction, which aims to predict the future positions of all agents in a crowd scene, given their past trajectories, plays a vital role in improving the safety of autonomous driving vehicles. For heterogeneous agents, it is imperative to account for the gap in feature distribution differences between agents in different categories. Besides, exploring the reference relationship between the future motions of agents is crucial yet overlooked in previous trajectory prediction methods. To tackle these challenges, we propose an edge-enhanced heterogeneous graph Transformer with priority-based feature aggregation for multi-modal trajectory prediction. Specifically, a new edge-enhanced heterogeneous interaction module that carries relative position information via edges is proposed to explore the complex interaction among agents. Additionally, we propose the concept of priority during the decoding phase and the corresponding measuring method, based on which a priority-based feature aggregation module is presented to enable referencing between agents, allowing for a more reasonable trajectory generation process. Additionally, we design an effective feature fusion method based on state refinement LSTM so that temporal and social features can be well integrated while accounting for their roles in trajectory prediction. Extensive experimental results on public datasets demonstrate that our approach outperforms the state-of-the-art baseline methods, confirming the effectiveness of our proposed method. The source code of our EPHGT model will be publicly released at https://github.com/xbchen82/EPHGT.
{"title":"Edge-Enhanced Heterogeneous Graph Transformer With Priority-Based Feature Aggregation for Multi-Agent Trajectory Prediction","authors":"Xiangzheng Zhou;Xiaobo Chen;Jian Yang","doi":"10.1109/TITS.2024.3509954","DOIUrl":"https://doi.org/10.1109/TITS.2024.3509954","url":null,"abstract":"Trajectory prediction, which aims to predict the future positions of all agents in a crowd scene, given their past trajectories, plays a vital role in improving the safety of autonomous driving vehicles. For heterogeneous agents, it is imperative to account for the gap in feature distribution differences between agents in different categories. Besides, exploring the reference relationship between the future motions of agents is crucial yet overlooked in previous trajectory prediction methods. To tackle these challenges, we propose an edge-enhanced heterogeneous graph Transformer with priority-based feature aggregation for multi-modal trajectory prediction. Specifically, a new edge-enhanced heterogeneous interaction module that carries relative position information via edges is proposed to explore the complex interaction among agents. Additionally, we propose the concept of priority during the decoding phase and the corresponding measuring method, based on which a priority-based feature aggregation module is presented to enable referencing between agents, allowing for a more reasonable trajectory generation process. Additionally, we design an effective feature fusion method based on state refinement LSTM so that temporal and social features can be well integrated while accounting for their roles in trajectory prediction. Extensive experimental results on public datasets demonstrate that our approach outperforms the state-of-the-art baseline methods, confirming the effectiveness of our proposed method. The source code of our EPHGT model will be publicly released at <uri>https://github.com/xbchen82/EPHGT</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"2266-2281"},"PeriodicalIF":7.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183956","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 : 2024-12-18DOI: 10.1109/TITS.2024.3510532
Xiaocui Li;Dan He;Xinyu Zhang
The challenge of optimally placing facilities to maximize coverage within road networks is a critical problem with significant implications for urban planning, emergency response, and the development of sustainable infrastructure. For instance, strategically locating fire stations or electric vehicle (EV) charging stations along a road network can greatly enhance public safety and support the adoption of clean transportation technologies. However, determining these optimal placements is computationally challenging, particularly when accounting for factors like road network distances and coverage radius. Traditional methods, such as greedy algorithms, offer a reasonable approximation but are limited by high computational complexity, making them less suitable for large-scale transportation networks. In response, our research introduces two novel algorithms designed to improve both the efficiency and scalability of the k-radius coverage problem. The first algorithm achieves a strong approximation with significantly reduced time complexity, while the second employs a sketch-based approach, offering a nearly linear time complexity relative to the number of edges. Although the second algorithm sacrifices some approximation accuracy, it offers substantial gains in computational speed, making it particularly valuable for large-scale transportation networks. Extensive experiments on large-scale real-world road networks demonstrate the superior performance of our proposed methods compared to existing solutions.
{"title":"Efficient Algorithms for Approximate k-Radius Coverage Query on Large-Scale Road Networks","authors":"Xiaocui Li;Dan He;Xinyu Zhang","doi":"10.1109/TITS.2024.3510532","DOIUrl":"https://doi.org/10.1109/TITS.2024.3510532","url":null,"abstract":"The challenge of optimally placing facilities to maximize coverage within road networks is a critical problem with significant implications for urban planning, emergency response, and the development of sustainable infrastructure. For instance, strategically locating fire stations or electric vehicle (EV) charging stations along a road network can greatly enhance public safety and support the adoption of clean transportation technologies. However, determining these optimal placements is computationally challenging, particularly when accounting for factors like road network distances and coverage radius. Traditional methods, such as greedy algorithms, offer a reasonable approximation but are limited by high computational complexity, making them less suitable for large-scale transportation networks. In response, our research introduces two novel algorithms designed to improve both the efficiency and scalability of the k-radius coverage problem. The first algorithm achieves a strong approximation with significantly reduced time complexity, while the second employs a sketch-based approach, offering a nearly linear time complexity relative to the number of edges. Although the second algorithm sacrifices some approximation accuracy, it offers substantial gains in computational speed, making it particularly valuable for large-scale transportation networks. Extensive experiments on large-scale real-world road networks demonstrate the superior performance of our proposed methods compared to existing solutions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1631-1644"},"PeriodicalIF":7.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183954","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}
Roadside units (RSUs) play a vital role in intelligent transportation systems (ITS), working as critical elements in delivering superior Internet of Vehicles (IoV) services. A large service coverage and fast accident information diffusion RSU deployment solution can reliably ensure the ITS’ quality of service. Simultaneously, with the development of the city and the ITS, changes in traffic flow lead to RSU load imbalance, which will reduce the benefit of the original RSU deployment, and it is necessary to adjust RSU locations with minimal cost. Besides, due to the high visibility of the ITS, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment work. Specifically, Sybil attack is one of the most dangerous attacks against ITS, and it can reshape the network state by forging multiple identities, interfering with risk sensing, etc. Motivated by these, we respectively propose the PSO-meme joint heuristic deployment algorithm (PJHDA) and the heuristic RSU multi-objective adaptation adjustment algorithm (HRMA3) to carry out deployment and adaptation adjustment of the city’s RSUs, taking into account the constraint of Sybil attack detection. Numerical results demonstrate that the multi-attribute performance of PJHDA is superior to the existing schemes. Compared with benchmark schemes, the HRMA3 excels in achieving advanced service coverage and load balancing while controlling costs, and both proposed schemes exhibit higher Sybil attack detection rate.
{"title":"Achieving Multi-Attribute Superiority and Sybil Attack Detection in IoV: A Heuristic-Based Dynamic RSU Deployment Scheme","authors":"Hongzhi Guo;Xinhan Wu;Zishuo Yin;Bomin Mao;Yijie Xun;Jiajia Liu;Wu Chen","doi":"10.1109/TITS.2024.3509980","DOIUrl":"https://doi.org/10.1109/TITS.2024.3509980","url":null,"abstract":"Roadside units (RSUs) play a vital role in intelligent transportation systems (ITS), working as critical elements in delivering superior Internet of Vehicles (IoV) services. A large service coverage and fast accident information diffusion RSU deployment solution can reliably ensure the ITS’ quality of service. Simultaneously, with the development of the city and the ITS, changes in traffic flow lead to RSU load imbalance, which will reduce the benefit of the original RSU deployment, and it is necessary to adjust RSU locations with minimal cost. Besides, due to the high visibility of the ITS, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment work. Specifically, Sybil attack is one of the most dangerous attacks against ITS, and it can reshape the network state by forging multiple identities, interfering with risk sensing, etc. Motivated by these, we respectively propose the PSO-meme joint heuristic deployment algorithm (PJHDA) and the heuristic RSU multi-objective adaptation adjustment algorithm (HRMA3) to carry out deployment and adaptation adjustment of the city’s RSUs, taking into account the constraint of Sybil attack detection. Numerical results demonstrate that the multi-attribute performance of PJHDA is superior to the existing schemes. Compared with benchmark schemes, the HRMA3 excels in achieving advanced service coverage and load balancing while controlling costs, and both proposed schemes exhibit higher Sybil attack detection rate.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"2734-2746"},"PeriodicalIF":7.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183932","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 : 2024-12-17DOI: 10.1109/TITS.2024.3511998
Youjie Yao;Cuiyu Wang;Xinyu Li;Liang Gao
Intelligent transportation systems, incorporating multiple AGVs, are extensively utilized in manufacturing workshops in various industries. This widespread use has spurred significant research interest in the integrated production and transportation scheduling problem, particularly in job shop environments. However, current research often fails to adequately leverage domain knowledge, leading to algorithms that struggle to find high-quality solutions for large-scale problems. To address this issue, this paper proposes a knowledge-driven hybrid algorithm (KDHA). The domain knowledge incorporated in the KDHA includes: 1) three critical path-based neighborhood structures for comprehensive neighborhood solution searches, 2) three neighborhood cropping methods to avoid ineffective searches for poor solutions, and 3) a new fast evaluation method to enhance the efficiency of neighborhood solution searching. Additionally, a new encoding method is introduced to achieve a one-to-one mapping between the chromosome and the disjunctive graph, allowing valuable information from neighborhood solutions to contribute to the algorithm’s evolution. Comparative experiments between the proposed algorithm and other state-of-the-art approaches are conducted on the small-scale EX and large-scale SWV benchmarks. The results demonstrate that the proposed KDHA is able to output better solutions efficiently and consistently, and updates the best solutions of all 20 SWV instances.
{"title":"A Knowledge-Driven Hybrid Algorithm for Solving the Integrated Production and Transportation Scheduling Problem in Job Shop","authors":"Youjie Yao;Cuiyu Wang;Xinyu Li;Liang Gao","doi":"10.1109/TITS.2024.3511998","DOIUrl":"https://doi.org/10.1109/TITS.2024.3511998","url":null,"abstract":"Intelligent transportation systems, incorporating multiple AGVs, are extensively utilized in manufacturing workshops in various industries. This widespread use has spurred significant research interest in the integrated production and transportation scheduling problem, particularly in job shop environments. However, current research often fails to adequately leverage domain knowledge, leading to algorithms that struggle to find high-quality solutions for large-scale problems. To address this issue, this paper proposes a knowledge-driven hybrid algorithm (KDHA). The domain knowledge incorporated in the KDHA includes: 1) three critical path-based neighborhood structures for comprehensive neighborhood solution searches, 2) three neighborhood cropping methods to avoid ineffective searches for poor solutions, and 3) a new fast evaluation method to enhance the efficiency of neighborhood solution searching. Additionally, a new encoding method is introduced to achieve a one-to-one mapping between the chromosome and the disjunctive graph, allowing valuable information from neighborhood solutions to contribute to the algorithm’s evolution. Comparative experiments between the proposed algorithm and other state-of-the-art approaches are conducted on the small-scale EX and large-scale SWV benchmarks. The results demonstrate that the proposed KDHA is able to output better solutions efficiently and consistently, and updates the best solutions of all 20 SWV instances.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"2707-2720"},"PeriodicalIF":7.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106237","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 : 2024-12-17DOI: 10.1109/TITS.2024.3512501
Rongyong Zhao;Bingyu Wei;Chuanfeng Han;Ping Jia;Wenjie Zhu;Cuiling Li;Yunlong Ma
Panic behaviors in a pedestrian flow often lead to a state of chaos or disorder among the pedestrian crowd, resulting in a crowd accident with high possibility. To investigate the panic pedestrian dynamics and further prevent serious crowd accidents, simulation based on dynamics modeling and accident video data is a popular solution to date. Thereby, it is challenging but significant to improve the crowd dynamics model more consistent with the ground truth of real pedestrian movement scenarios, with consideration of both physical contact force and panic emotional propagation in a crowd. Therefore, this study proposed an extended social force model (ESFM) by applying the physical contact-force estimation during pedestrian collision based on non-smooth contact dynamics. Subsequently, the ESFM was integrated with an improved panic propagation model (IPPM) considering obstacle and promotion factors. Finally, taking the crowd panic accident happened in Nepal in 2015 as an experiment case, the simulation of panic crowd dynamics was conducted within Anylogic software. Four cases of SFM, ESFM, SFM+IPPM, and ESFM+IPPM were compared quantitatively and graphically. The experimental results showed that the pedestrian distribution obtained from the proposed ESFM+IPPM was the closest to the ground truth during the panic response period, with 28.8% lower of Hausdorff distance than the original SFM, and 21.6% lower the well-known BHSFM, respectively. This approach can help improve the panic crowd modeling and pedestrian distribution prediction in real scenarios.
{"title":"Improved Crowd Dynamics Analysis Considering Physical Contact Force and Panic Emotional Propagation","authors":"Rongyong Zhao;Bingyu Wei;Chuanfeng Han;Ping Jia;Wenjie Zhu;Cuiling Li;Yunlong Ma","doi":"10.1109/TITS.2024.3512501","DOIUrl":"https://doi.org/10.1109/TITS.2024.3512501","url":null,"abstract":"Panic behaviors in a pedestrian flow often lead to a state of chaos or disorder among the pedestrian crowd, resulting in a crowd accident with high possibility. To investigate the panic pedestrian dynamics and further prevent serious crowd accidents, simulation based on dynamics modeling and accident video data is a popular solution to date. Thereby, it is challenging but significant to improve the crowd dynamics model more consistent with the ground truth of real pedestrian movement scenarios, with consideration of both physical contact force and panic emotional propagation in a crowd. Therefore, this study proposed an extended social force model (ESFM) by applying the physical contact-force estimation during pedestrian collision based on non-smooth contact dynamics. Subsequently, the ESFM was integrated with an improved panic propagation model (IPPM) considering obstacle and promotion factors. Finally, taking the crowd panic accident happened in Nepal in 2015 as an experiment case, the simulation of panic crowd dynamics was conducted within Anylogic software. Four cases of SFM, ESFM, SFM+IPPM, and ESFM+IPPM were compared quantitatively and graphically. The experimental results showed that the pedestrian distribution obtained from the proposed ESFM+IPPM was the closest to the ground truth during the panic response period, with 28.8% lower of Hausdorff distance than the original SFM, and 21.6% lower the well-known BHSFM, respectively. This approach can help improve the panic crowd modeling and pedestrian distribution prediction in real scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1840-1851"},"PeriodicalIF":7.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183995","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}
RGBT tracking is a task that utilizes images from visible (RGB) and thermal infrared (TIR) modalities to continuously locate a target, which plays an important role in various fields including intelligent transportation systems. Most existing RGBT trackers do not achieve high precision and real-time tracking speed simultaneously. To address this challenge, we propose an innovative RGBT tracker, the Siamese Triple-stream Feature Aggregation Network (SiamTFA). Firstly, a triple-stream backbone is presented to implement multi-modal feature extraction and fusion, which contains two parallel Swin Transformer feature extraction streams, and one feature fusion stream composed of joint-complementary feature aggregation (JCFA) modules. Secondly, our proposed JCFA module utilizes a joint-complementary attention to guide the aggregation of multi-modal features. Specifically, the joint attention can focus on spatial location information and semantic information of the target by combining the features of two modalities. Considering the complementarity between RGB and TIR modalities, the complementary attention is introduced to enhance the information of beneficial modality and suppress the information of ineffective modality. Thirdly, in order to reduce the computational complexity of the joint-complementary attention, we propose a depthwise shared attention structure, which utilizes depthwise convolution and shared features to achieve lightweight attention. Finally, we conduct extensive experiments on four official RGBT test datasets and the experimental results demonstrate that our proposed tracker outperforms some state-of-the-art trackers and the tracking speed reaches 37 frames per second (FPS). The code is available at https://github.com/zjjqinyu/SiamTFA.
{"title":"SiamTFA: Siamese Triple-Stream Feature Aggregation Network for Efficient RGBT Tracking","authors":"Jianming Zhang;Yu Qin;Shimeng Fan;Zhu Xiao;Jin Zhang","doi":"10.1109/TITS.2024.3512551","DOIUrl":"https://doi.org/10.1109/TITS.2024.3512551","url":null,"abstract":"RGBT tracking is a task that utilizes images from visible (RGB) and thermal infrared (TIR) modalities to continuously locate a target, which plays an important role in various fields including intelligent transportation systems. Most existing RGBT trackers do not achieve high precision and real-time tracking speed simultaneously. To address this challenge, we propose an innovative RGBT tracker, the Siamese Triple-stream Feature Aggregation Network (SiamTFA). Firstly, a triple-stream backbone is presented to implement multi-modal feature extraction and fusion, which contains two parallel Swin Transformer feature extraction streams, and one feature fusion stream composed of joint-complementary feature aggregation (JCFA) modules. Secondly, our proposed JCFA module utilizes a joint-complementary attention to guide the aggregation of multi-modal features. Specifically, the joint attention can focus on spatial location information and semantic information of the target by combining the features of two modalities. Considering the complementarity between RGB and TIR modalities, the complementary attention is introduced to enhance the information of beneficial modality and suppress the information of ineffective modality. Thirdly, in order to reduce the computational complexity of the joint-complementary attention, we propose a depthwise shared attention structure, which utilizes depthwise convolution and shared features to achieve lightweight attention. Finally, we conduct extensive experiments on four official RGBT test datasets and the experimental results demonstrate that our proposed tracker outperforms some state-of-the-art trackers and the tracking speed reaches 37 frames per second (FPS). The code is available at <uri>https://github.com/zjjqinyu/SiamTFA</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1900-1913"},"PeriodicalIF":7.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183978","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 : 2024-12-17DOI: 10.1109/TITS.2024.3510412
Xinkun Tao;Chengcheng Fu;Zhuang Xiao;Qingyuan Wang;Xiaoyun Feng;Pengfei Sun
Freight trains operate in dynamic environments and exhibit time-varying behavior, making static mechanistic models inadequate for capturing these changes. This often results in impractical predictions of train operational states and optimization outcomes. To facilitate planning in such operational conditions, this paper proposes a dynamic modeling method to assess energy consumption and the voltage of traction power supply system (TPSS), and a large-scale adaptive multi-strategy multi-objective competitive swarm optimization algorithm (LA-MOCSO) for solving dynamic optimization challenges. Specific, a mechanistic “train-track-power grid” (TTP) model is first built to calculate power flow and TPSS voltage during multiple train operations. Second, a hybrid modeling approach that combines the mechanistic model and data-driven models is proposed to account for variations in train and environmental characteristics, and a multi-objective optimization model is established aimed at improving energy-efficiency and voltage stability of TPSS. Then, to tackle the complexities of the multi-objective optimization problem, an LA-MOCSO algorithm is proposed, which can be applied to solve the large-scale optimization problem of multi-train long-distance routes. Finally, the high accuracy of the dynamic model was validated with measurement data; the performance and computational efficiency of LA-MOCSO was verified through five algorithms; the comprehensive optimization method can, through the allocation and utilization of regenerative braking energy, further reduce substation energy consumption and maintain grid voltage stability.
{"title":"Dynamic Modeling and Solving Methods for Multi-Train Energy-Efficient Operation and Network Voltage Stability","authors":"Xinkun Tao;Chengcheng Fu;Zhuang Xiao;Qingyuan Wang;Xiaoyun Feng;Pengfei Sun","doi":"10.1109/TITS.2024.3510412","DOIUrl":"https://doi.org/10.1109/TITS.2024.3510412","url":null,"abstract":"Freight trains operate in dynamic environments and exhibit time-varying behavior, making static mechanistic models inadequate for capturing these changes. This often results in impractical predictions of train operational states and optimization outcomes. To facilitate planning in such operational conditions, this paper proposes a dynamic modeling method to assess energy consumption and the voltage of traction power supply system (TPSS), and a large-scale adaptive multi-strategy multi-objective competitive swarm optimization algorithm (LA-MOCSO) for solving dynamic optimization challenges. Specific, a mechanistic “train-track-power grid” (TTP) model is first built to calculate power flow and TPSS voltage during multiple train operations. Second, a hybrid modeling approach that combines the mechanistic model and data-driven models is proposed to account for variations in train and environmental characteristics, and a multi-objective optimization model is established aimed at improving energy-efficiency and voltage stability of TPSS. Then, to tackle the complexities of the multi-objective optimization problem, an LA-MOCSO algorithm is proposed, which can be applied to solve the large-scale optimization problem of multi-train long-distance routes. Finally, the high accuracy of the dynamic model was validated with measurement data; the performance and computational efficiency of LA-MOCSO was verified through five algorithms; the comprehensive optimization method can, through the allocation and utilization of regenerative braking energy, further reduce substation energy consumption and maintain grid voltage stability.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"2040-2056"},"PeriodicalIF":7.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183811","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}
In this paper, we propose an accurate and robust multi-modal sensor fusion framework, MetroLoc, towards one of the most extreme scenarios, the large-scale metro environments. MetroLoc is built atop an IMU-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, and inertial information with the convenience of loosely coupled methods. The proposed framework is composed of three submodules: IMU odometry, LiDAR-inertial odometry (LIO), and Visual-inertial odometry (VIO). The IMU is treated as the primary sensor, which achieves the observations from LIO and VIO to constrain the accelerometer and gyroscope biases. Compared to previous point-only LIO methods, our approach leverages more geometry information by introducing both line and plane features into motion estimation. The VIO also utilizes the environmental structure information by employing both lines and points. Our proposed method has been tested in the long-during metro environments with a maintenance vehicle. Experimental results show the system more accurate and robust than the state-of-the-art approaches with real-time performance. The proposed method can reach 0.278% maximum drift in translation even in the highly degenerated tunnels. Besides, we develop a series of Virtual Reality (VR) applications towards efficient, economical, and interactive rail vehicle state and trackside infrastructure monitoring tasks.
{"title":"MetroLoc: Metro Vehicle Mapping and Localization With LiDAR-Camera-Inertial Integration","authors":"Yusheng Wang;Weiwei Song;Yapeng Wang;Xinye Dai;Yidong Lou","doi":"10.1109/TITS.2024.3512000","DOIUrl":"https://doi.org/10.1109/TITS.2024.3512000","url":null,"abstract":"In this paper, we propose an accurate and robust multi-modal sensor fusion framework, MetroLoc, towards one of the most extreme scenarios, the large-scale metro environments. MetroLoc is built atop an IMU-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, and inertial information with the convenience of loosely coupled methods. The proposed framework is composed of three submodules: IMU odometry, LiDAR-inertial odometry (LIO), and Visual-inertial odometry (VIO). The IMU is treated as the primary sensor, which achieves the observations from LIO and VIO to constrain the accelerometer and gyroscope biases. Compared to previous point-only LIO methods, our approach leverages more geometry information by introducing both line and plane features into motion estimation. The VIO also utilizes the environmental structure information by employing both lines and points. Our proposed method has been tested in the long-during metro environments with a maintenance vehicle. Experimental results show the system more accurate and robust than the state-of-the-art approaches with real-time performance. The proposed method can reach 0.278% maximum drift in translation even in the highly degenerated tunnels. Besides, we develop a series of Virtual Reality (VR) applications towards efficient, economical, and interactive rail vehicle state and trackside infrastructure monitoring tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1441-1453"},"PeriodicalIF":7.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183812","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 : 2024-12-16DOI: 10.1109/TITS.2024.3510041
Surya Soman;Mario Zanon;Alberto Bemporad
Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, such as the position other vehicles will take while crossing an uncontrolled intersection. We address this problem by proposing a stochastic model predictive control (MPC) approach with robust collision avoidance constraints to guarantee safety. By adopting a stochastic formulation, the quality of closed-loop tracking is increased by avoiding giving excessive importance to future obstacle configurations that are unlikely to occur. We compute the probabilities associated with different obstacle trajectories by learning a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation on a simulated realistic intersection.
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