This study investigates the role of convolutional layers in deep convolutional neural networks for scenarios involving interactions between the users and road-interaction environment. The user-driving behaviour and road environment exhibit unique features, and applications often require training-specific models for each user-environment pair. This leads to significant data collection and computational demands, including model training, thus necessitating efficient solutions to minimise these requirements. The aim is to determine whether the convolutional layers of deep convolutional autoencoders (DCAEs) are more specific to the user or the environment. The distinction might lead to optimising training strategies that reduce resource requirements. Case studies and data collection are performed on multiple bicyclists navigating various road segments. We evaluated the specificity of convolutional layers using metrics such as training epochs, execution time, model and parameter loading times, and total training loss. The results confirmed that the patterns learned by the outer convolutional layers are predominantly user-specific, emphasising individual behaviour over road-environment factors. This user-specific pattern recognition enhances model efficiency, reduces data requirements, and improves accuracy in predicting user behaviour across varying environments. Furthermore, after analysing training strategies, we found that complete refinement provided higher accuracy and stability at the cost of longer training and loading times. By contrast, freezing layers allowed faster initialisation but might necessitate extended training in complex cases. Finally, we examined the implications of these findings for improving safety and performance in driver-road interactions.
{"title":"Efficient Deep Learning for Driver-Road Interactions: The Role of Convolutional Layer Specificity in Reducing Data Requirements","authors":"Shumayla Yaqoob;Giacomo Morabito;Salvatore Damiano Cafiso;Giuseppina Pappalardo;Ikram Syed;Farman Ullah","doi":"10.1109/TITS.2025.3630630","DOIUrl":"https://doi.org/10.1109/TITS.2025.3630630","url":null,"abstract":"This study investigates the role of convolutional layers in deep convolutional neural networks for scenarios involving interactions between the users and road-interaction environment. The user-driving behaviour and road environment exhibit unique features, and applications often require training-specific models for each user-environment pair. This leads to significant data collection and computational demands, including model training, thus necessitating efficient solutions to minimise these requirements. The aim is to determine whether the convolutional layers of deep convolutional autoencoders (DCAEs) are more specific to the user or the environment. The distinction might lead to optimising training strategies that reduce resource requirements. Case studies and data collection are performed on multiple bicyclists navigating various road segments. We evaluated the specificity of convolutional layers using metrics such as training epochs, execution time, model and parameter loading times, and total training loss. The results confirmed that the patterns learned by the outer convolutional layers are predominantly user-specific, emphasising individual behaviour over road-environment factors. This user-specific pattern recognition enhances model efficiency, reduces data requirements, and improves accuracy in predicting user behaviour across varying environments. Furthermore, after analysing training strategies, we found that complete refinement provided higher accuracy and stability at the cost of longer training and loading times. By contrast, freezing layers allowed faster initialisation but might necessitate extended training in complex cases. Finally, we examined the implications of these findings for improving safety and performance in driver-road interactions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 2","pages":"2729-2740"},"PeriodicalIF":8.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223706","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}
The min-max multiple traveling salesman problem (min-max mTSP) is a significant variant of the min-max routing problem, focusing on minimizing the longest subtour cost among multiple salesmen working cooperatively. This problem is highly relevant in real-world scenarios but is notoriously challenging, especially as the scale increases with numerous salesmen covering thousands of cities. This paper presents a novel approach for solving large-scale min-max mTSP. Our method, based on deep reinforcement learning, introduces a novel two-stage process. In the first stage, we generate an initial solution using a constructive model incorporating global and local attention mechanisms through a gated network. Additionally, we employ multi-task training on a single constructive model across various mTSP problems with differing numbers of salesmen, using weighted task balancing to balance the multi-task learning process. In the second stage, the initial solution is iteratively refined using improvement policy, which re-optimizes the current subtours to form a new better one. To the best of our knowledge, our method is the first capable of handling problems with up to 10,000 nodes. The experimental results demonstrate that our approach achieves the best solution on 71% of the problems in randomly uniform datasets, outperforming all existing methods. Our code is available at https://github.com/1hhix/CMIP
{"title":"CMIP: Combining Constructive Model With Improvement Policy for Large-Scale Min-Max Multiple Traveling Salesman Problem","authors":"Binbin Zuo;Weifan Li;Jiankuo Zhao;Tianxiang Bai;Linqian Yang;Zhe Ma;Yuanheng Zhu","doi":"10.1109/TITS.2025.3632076","DOIUrl":"https://doi.org/10.1109/TITS.2025.3632076","url":null,"abstract":"The min-max multiple traveling salesman problem (min-max mTSP) is a significant variant of the min-max routing problem, focusing on minimizing the longest subtour cost among multiple salesmen working cooperatively. This problem is highly relevant in real-world scenarios but is notoriously challenging, especially as the scale increases with numerous salesmen covering thousands of cities. This paper presents a novel approach for solving large-scale min-max mTSP. Our method, based on deep reinforcement learning, introduces a novel two-stage process. In the first stage, we generate an initial solution using a constructive model incorporating global and local attention mechanisms through a gated network. Additionally, we employ multi-task training on a single constructive model across various mTSP problems with differing numbers of salesmen, using weighted task balancing to balance the multi-task learning process. In the second stage, the initial solution is iteratively refined using improvement policy, which re-optimizes the current subtours to form a new better one. To the best of our knowledge, our method is the first capable of handling problems with up to 10,000 nodes. The experimental results demonstrate that our approach achieves the best solution on 71% of the problems in randomly uniform datasets, outperforming all existing methods. Our code is available at <uri>https://github.com/1hhix/CMIP</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1550-1564"},"PeriodicalIF":8.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877120","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 : 2025-11-18DOI: 10.1109/TITS.2025.3631922
Akshay Gupta;Pushpa Choudhary;Manoranjan Parida
Nighttime driving presents unique challenges and risks compared to daytime driving. This study analyzed rear-end conflicts on expressways and identified thresholds for various conflict indicators under both day and night conditions. Utilizing cost-effective 3D LiDAR technology, renowned for its robustness in low-light environments, this study elucidates the multifaceted influence of various factors on traffic safety dynamics across day and night conditions. Extreme value theory was applied to evaluate safety, incorporating factors like traffic environment and driver characteristics that are often overlooked in naturalistic studies. The analysis also included the effect of percentage oblique width on safety-critical events. Interestingly, drivers experienced about three times higher crash risks during the day compared to night, likely due to increased vigilance and caution at night. These findings offer valuable recommendations for setting headway requirements based on lighting conditions and can help improve advanced driver assistance systems to detect and respond more effectively to unsafe following distances.
{"title":"Advanced Sensor Analytics and Extreme Value Modeling: Dichotomizing Day–Night Variability in Rear-End Collisions on Expressways","authors":"Akshay Gupta;Pushpa Choudhary;Manoranjan Parida","doi":"10.1109/TITS.2025.3631922","DOIUrl":"https://doi.org/10.1109/TITS.2025.3631922","url":null,"abstract":"Nighttime driving presents unique challenges and risks compared to daytime driving. This study analyzed rear-end conflicts on expressways and identified thresholds for various conflict indicators under both day and night conditions. Utilizing cost-effective 3D LiDAR technology, renowned for its robustness in low-light environments, this study elucidates the multifaceted influence of various factors on traffic safety dynamics across day and night conditions. Extreme value theory was applied to evaluate safety, incorporating factors like traffic environment and driver characteristics that are often overlooked in naturalistic studies. The analysis also included the effect of percentage oblique width on safety-critical events. Interestingly, drivers experienced about three times higher crash risks during the day compared to night, likely due to increased vigilance and caution at night. These findings offer valuable recommendations for setting headway requirements based on lighting conditions and can help improve advanced driver assistance systems to detect and respond more effectively to unsafe following distances.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1499-1510"},"PeriodicalIF":8.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877113","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}
Generative models have shown promising results in capturing human mobility characteristics and generating synthetic trajectories. However, it remains challenging to ensure that the generated geospatial mobility data is semantically realistic, including consistent location sequences, and reflects real-world characteristics, such as constraining on geospatial limits. We reformat human mobility modeling as an autoregressive generation task to address these issues, leveraging the Generative Pre-trained Transformer (GPT) architecture. To ensure its controllable generation to alleviate the above challenges, we propose a geospatially-aware generative model, MobilityGPT. We propose a gravity-based sampling method to train a transformer for semantic sequence similarity. Then, we constrained the training process via a road connectivity matrix that provides the connectivity of sequences in trajectory generation, thereby keeping generated trajectories in geospatial limits. Lastly, we proposed to construct a preference dataset for fine-tuning MobilityGPT via Reinforcement Learning from Trajectory Feedback (RLTF) mechanism, which minimizes the travel distance between training and the synthetically generated trajectories. Experiments on real-world datasets demonstrate MobilityGPT’s superior performance over state-of-the-art methods in generating high-quality mobility trajectories that are closest to real data in terms of origin-destination similarity, trip length, travel radius, link, and gravity distributions. We release the source code and reference links to datasets at https://github.com/ammarhydr/MobilityGPT
{"title":"MobilityGPT: Enhanced Human Mobility Modeling With a GPT Model","authors":"Ammar Haydari;Dongjie Chen;Zhengfeng Lai;Michael Zhang;Chen-Nee Chuah","doi":"10.1109/TITS.2025.3626357","DOIUrl":"https://doi.org/10.1109/TITS.2025.3626357","url":null,"abstract":"Generative models have shown promising results in capturing human mobility characteristics and generating synthetic trajectories. However, it remains challenging to ensure that the generated geospatial mobility data is semantically realistic, including consistent location sequences, and reflects real-world characteristics, such as constraining on geospatial limits. We reformat human mobility modeling as an autoregressive generation task to address these issues, leveraging the Generative Pre-trained Transformer (GPT) architecture. To ensure its controllable generation to alleviate the above challenges, we propose a geospatially-aware generative model, MobilityGPT. We propose a gravity-based sampling method to train a transformer for semantic sequence similarity. Then, we constrained the training process via a road connectivity matrix that provides the connectivity of sequences in trajectory generation, thereby keeping generated trajectories in geospatial limits. Lastly, we proposed to construct a preference dataset for fine-tuning MobilityGPT via Reinforcement Learning from Trajectory Feedback (RLTF) mechanism, which minimizes the travel distance between training and the synthetically generated trajectories. Experiments on real-world datasets demonstrate MobilityGPT’s superior performance over state-of-the-art methods in generating high-quality mobility trajectories that are closest to real data in terms of origin-destination similarity, trip length, travel radius, link, and gravity distributions. We release the source code and reference links to datasets at <uri>https://github.com/ammarhydr/MobilityGPT</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1681-1694"},"PeriodicalIF":8.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877115","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 : 2025-11-11DOI: 10.1109/TITS.2025.3623579
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3623579","DOIUrl":"https://doi.org/10.1109/TITS.2025.3623579","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"C3-C3"},"PeriodicalIF":8.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11241053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486509","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 : 2025-11-10DOI: 10.1109/TITS.2025.3625181
Quan Hao;Rui Shi;Jiaze Li;Liguo Zhang
Foreign object intrusion into high-speed railway (HSR) catenary systems poses severe operational hazards, making effective detection crucial for safety. Precise detection of these small intrusive objects is essential. However, the lack of datasets and research on foreign object intrusion in HSR scenario brings two major challenges: limited data and low accuracy for detecting small intrusive objects. To address these challenges, this paper introduces a novel generative method for detecting foreign object intrusion. To address data limitations, we use low-rank adaptation to fine-tune a diffusion model, developing a generation-extraction-integration framework that generates true-to-reality HSR images of small intrusive target objects. Furthermore, to enhance the detection of small objects in HSR scenario, we propose a new detection model called SA-YOLO. Based on the YOLOv9 architecture, this model optimizes the backbone network using the star operation, an element-wise multiplication method, and introduces the A-DyS module to improve upsampling through dynamic sampling and attention mechanism. Extensive experiments demonstrate that in the HSR scenario our method outperforms existing state-of-the-art approaches in terms of both generation quality and detection performance, while also showing high robustness.
{"title":"Generative Approach for Detecting Small Intrusive Foreign Objects in High-Speed Railway Scenario","authors":"Quan Hao;Rui Shi;Jiaze Li;Liguo Zhang","doi":"10.1109/TITS.2025.3625181","DOIUrl":"https://doi.org/10.1109/TITS.2025.3625181","url":null,"abstract":"Foreign object intrusion into high-speed railway (HSR) catenary systems poses severe operational hazards, making effective detection crucial for safety. Precise detection of these small intrusive objects is essential. However, the lack of datasets and research on foreign object intrusion in HSR scenario brings two major challenges: limited data and low accuracy for detecting small intrusive objects. To address these challenges, this paper introduces a novel generative method for detecting foreign object intrusion. To address data limitations, we use low-rank adaptation to fine-tune a diffusion model, developing a generation-extraction-integration framework that generates true-to-reality HSR images of small intrusive target objects. Furthermore, to enhance the detection of small objects in HSR scenario, we propose a new detection model called SA-YOLO. Based on the YOLOv9 architecture, this model optimizes the backbone network using the star operation, an element-wise multiplication method, and introduces the A-DyS module to improve upsampling through dynamic sampling and attention mechanism. Extensive experiments demonstrate that in the HSR scenario our method outperforms existing state-of-the-art approaches in terms of both generation quality and detection performance, while also showing high robustness.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1471-1484"},"PeriodicalIF":8.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929525","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 : 2025-11-10DOI: 10.1109/TITS.2025.3616119
Changze Li;Yunxue Lu;Hao Wang
The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.
{"title":"A Multi-Objective Model for Traffic Signal Coordination Control With Queue Profile Estimation","authors":"Changze Li;Yunxue Lu;Hao Wang","doi":"10.1109/TITS.2025.3616119","DOIUrl":"https://doi.org/10.1109/TITS.2025.3616119","url":null,"abstract":"The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23389-23406"},"PeriodicalIF":8.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665752","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 : 2025-11-10DOI: 10.1109/TITS.2025.3625273
Qinghua Chen;Xin Ge;Shiqian Chen;Xiaoyu Hu;Yong Jiang;Jiheng Wu;Kaiyun Wang
Electronically controlled pneumatic (ECP) is an auxiliary device for the air brake system that replaces traditional signals with electrical signals for transmitting braking waves. This study presents an ECP design that integrates synchronous braking and release functionalities. Based on the fluid dynamics theory, we developed an air brake system model with ECP devices for a 20,000-ton heavy-haul train. Then, the influence of the ECP devices on the performance of air braking, longitudinal dynamics, and operational safety is analyzed under different operation conditions. Simulation results demonstrate that the ECP devices can significantly enhance the consistency of train manipulation under braking and release phases, and increase the charging time of the air brake system during cyclic braking. Additionally, the ECP devices effectively reduce the compressive coupler forces of the salve control locomotives and improve the wheel-rail safety of trains negotiating tight curves. The findings in this study could provide valuable guidance for parameter design when implementing ECP devices in field applications.
{"title":"An Air Brake Model With Electronically Controlled Pneumatic for Heavy-Haul Trains","authors":"Qinghua Chen;Xin Ge;Shiqian Chen;Xiaoyu Hu;Yong Jiang;Jiheng Wu;Kaiyun Wang","doi":"10.1109/TITS.2025.3625273","DOIUrl":"https://doi.org/10.1109/TITS.2025.3625273","url":null,"abstract":"Electronically controlled pneumatic (ECP) is an auxiliary device for the air brake system that replaces traditional signals with electrical signals for transmitting braking waves. This study presents an ECP design that integrates synchronous braking and release functionalities. Based on the fluid dynamics theory, we developed an air brake system model with ECP devices for a 20,000-ton heavy-haul train. Then, the influence of the ECP devices on the performance of air braking, longitudinal dynamics, and operational safety is analyzed under different operation conditions. Simulation results demonstrate that the ECP devices can significantly enhance the consistency of train manipulation under braking and release phases, and increase the charging time of the air brake system during cyclic braking. Additionally, the ECP devices effectively reduce the compressive coupler forces of the salve control locomotives and improve the wheel-rail safety of trains negotiating tight curves. The findings in this study could provide valuable guidance for parameter design when implementing ECP devices in field applications.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1578-1591"},"PeriodicalIF":8.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877112","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}
Recently, multi-sensor fusion-based vehicle infrastructure cooperative perception has aroused extensive attention due to the demands for the safety of autonomous driving and traffic monitoring. An accurate calibration between different sensors is a critical foundation for most sensor fusion systems. For LiDAR-camera calibration, high accuracy can be achieved with the help of artificial calibration targets, such as a checkerboard. However, unlike autonomous vehicles, roadside sensors monitor traffic scenes with continuous traffic flow from a fixed viewpoint, posing challenges for conventional calibration methods. There, a calibration method suitable for roadside scenes is required for infrastructure sensors. In this paper, we propose FlowCalib, a novel targetless infrastructure LiDAR-camera spatial calibration method through alignment of scene flow and optical flow. The main idea is to leverage the inherent consistency of moving objects in traffic flow across two types of sensor data. Firstly, the moving objects are extracted by optical flow and scene flow. Then, the extrinsic parameters are obtained in two steps: rough calibration and calibration refinement. In rough calibration, the center and motion flow of each moving instance are calculated by clustering methods separately in the point cloud and image. Based on this, the possible initial value set of extrinsic parameters is estimated by two-step parameter sampling. The initial parameters are obtained by distance of center and motion flow in point cloud and image based scoring. Subsequently, the extrinsic parameters are refined by optimization of instance alignment loss and flow alignment loss of moving objects. In the end, quantitative and qualitative experiments are conducted to validate the effectiveness of the algorithm across both simulated datasets and real-world datasets.
{"title":"FlowCalib: Targetless Infrastructure LiDAR-Camera Extrinsic Calibration Based on Optical Flow and Scene Flow","authors":"Renwei Hai;Yanqing Shen;Yuchen Yan;Shitao Chen;Jingmin Xin;Nanning Zheng","doi":"10.1109/TITS.2025.3627651","DOIUrl":"https://doi.org/10.1109/TITS.2025.3627651","url":null,"abstract":"Recently, multi-sensor fusion-based vehicle infrastructure cooperative perception has aroused extensive attention due to the demands for the safety of autonomous driving and traffic monitoring. An accurate calibration between different sensors is a critical foundation for most sensor fusion systems. For LiDAR-camera calibration, high accuracy can be achieved with the help of artificial calibration targets, such as a checkerboard. However, unlike autonomous vehicles, roadside sensors monitor traffic scenes with continuous traffic flow from a fixed viewpoint, posing challenges for conventional calibration methods. There, a calibration method suitable for roadside scenes is required for infrastructure sensors. In this paper, we propose FlowCalib, a novel targetless infrastructure LiDAR-camera spatial calibration method through alignment of scene flow and optical flow. The main idea is to leverage the inherent consistency of moving objects in traffic flow across two types of sensor data. Firstly, the moving objects are extracted by optical flow and scene flow. Then, the extrinsic parameters are obtained in two steps: rough calibration and calibration refinement. In rough calibration, the center and motion flow of each moving instance are calculated by clustering methods separately in the point cloud and image. Based on this, the possible initial value set of extrinsic parameters is estimated by two-step parameter sampling. The initial parameters are obtained by distance of center and motion flow in point cloud and image based scoring. Subsequently, the extrinsic parameters are refined by optimization of instance alignment loss and flow alignment loss of moving objects. In the end, quantitative and qualitative experiments are conducted to validate the effectiveness of the algorithm across both simulated datasets and real-world datasets.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1565-1577"},"PeriodicalIF":8.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877121","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 : 2025-11-10DOI: 10.1109/TITS.2025.3624271
Yunpeng Ba;Ruihao Zheng;Zhenkun Wang;Genghui Li
The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) aims to find optimal routes for vehicles with different capacities and costs, and is common in real-world applications. Total cost and fairness among drivers are two important yet conflicting objectives, while existing studies address either one objective alone or a specific weighted sum of them. To trade off the two objectives simultaneously, this paper formulates the Multi-Objective HFVRP (MO-HFVRP). Our analysis reveals that the MO-HFVRP is challenging, as the decision space has sparse feasible solutions and the objective space exhibits an uneven distribution of objective vectors. Subsequently, a corresponding algorithm called AMOILS/D is proposed. It decomposes the MO-HFVRP into a few single-objective subproblems, and then applies Iterated Local Search (ILS) and multi-objective optimization techniques to collaboratively solve them. AMOILS/D has three key components. The first is the resource allocation strategy that periodically selects subproblems to focus the search on promising regions. The other two are the adaptive perturbation degree control and the acceptance mechanism in ILS. They enable effective navigation of the decision space and balance convergence and diversity. Experimental results show that AMOILS/D significantly outperforms other representative algorithms across most instances. Ablation studies also confirm the effectiveness of each proposed component.
{"title":"Multi-Objective Heterogeneous Fleet Vehicle Routing Problem: Formulation and Algorithm","authors":"Yunpeng Ba;Ruihao Zheng;Zhenkun Wang;Genghui Li","doi":"10.1109/TITS.2025.3624271","DOIUrl":"https://doi.org/10.1109/TITS.2025.3624271","url":null,"abstract":"The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) aims to find optimal routes for vehicles with different capacities and costs, and is common in real-world applications. Total cost and fairness among drivers are two important yet conflicting objectives, while existing studies address either one objective alone or a specific weighted sum of them. To trade off the two objectives simultaneously, this paper formulates the Multi-Objective HFVRP (MO-HFVRP). Our analysis reveals that the MO-HFVRP is challenging, as the decision space has sparse feasible solutions and the objective space exhibits an uneven distribution of objective vectors. Subsequently, a corresponding algorithm called AMOILS/D is proposed. It decomposes the MO-HFVRP into a few single-objective subproblems, and then applies Iterated Local Search (ILS) and multi-objective optimization techniques to collaboratively solve them. AMOILS/D has three key components. The first is the resource allocation strategy that periodically selects subproblems to focus the search on promising regions. The other two are the adaptive perturbation degree control and the acceptance mechanism in ILS. They enable effective navigation of the decision space and balance convergence and diversity. Experimental results show that AMOILS/D significantly outperforms other representative algorithms across most instances. Ablation studies also confirm the effectiveness of each proposed component.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1666-1680"},"PeriodicalIF":8.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877117","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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