IEEE Transactions on Intelligent Transportation Systems最新文献

{"title":"Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms","authors":"Yonghang Li;Chang Wang;Yifei Wang;Miao Ren;Jin Niu;Jikang Zhao;Kai Du","doi":"10.1109/TITS.2025.3574763","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574763","url":null,"abstract":"The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. Firstly, a Memory Fruit-fly-optimizer with Individual Differences (MFID) is innovatively proposed to drive Multi-threshold Otsu (MOtsu)-based multi-class segmentation of lanes, which is a high-dimensional optimization task with real-time and local optimal challenges, for capturing lane clues obscured in multi-intensity reflections, consequently reducing missed detections. Specifically, to solve the challenges inherent in the task, the MFID incorporates a novel memory mechanism to establish fast-converging initial conditions for real-time detection, while creatively considering individual differences to motivate multi-swarm optimization that mitigates local optima risks. After integration, the MFID-MOtsu is constructed for lane segmentation. Subsequently, a dynamic discretization mechanism is proposed to efficiently separate lane edges from interference edges, mitigating accuracy degradation caused by their entanglement. Finally, the false detection issue is greatly reduced through the implementation of adaptive geometric filters. The experimental results demonstrate that the proposed method achieves an average accuracy of 93.21% on rainy nights, indicating an average improvement of 12.7% over state-of-the-art methods. Additionally, without any parameter modifications, the proposed method is applicable to both normal and classic challenging scenes, such as nights, tunnels, rainy days, and shadows. The algorithm achieves an average accuracy of 96.2% and an average detection speed of 46 frames per second.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9529-9541"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536407","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}

{"title":"Dissipating Traffic Waves in Mixed Vehicle Platoons: A Controller-Matching-Based Double-Layer Distributed Model Predictive Control Approach","authors":"Panshuo Li;Xingyan Mao;Chao Huang;Pengxu Li","doi":"10.1109/TITS.2025.3575540","DOIUrl":"https://doi.org/10.1109/TITS.2025.3575540","url":null,"abstract":"This study proposes a novel distributed model predictive control (DMPC) approach for mixed vehicle platoons (MVPs), which achieves driving safety, asymptotic stability, and traffic wave dissipation simultaneously. The longitudinal dynamics model and safety constraints are first established for each vehicle. The MVPs are divided into several sub-platoons according to the distribution of connected-and-automated vehicles (CAVs) and human-driven vehicles (HDVs). Then, a compound controller to ensure the head-to-tail string stability of MVPs is constructed as a reference controller for the subsequent design of the double-layer distributed model predictive controller (DL-DMPC). To describe the behavior of human drivers, a car-following model specific to HDVs is developed. On the basis of the designed compound controller and the description of HDVs, the DL-DMPC is proposed. The first layer improves tracking performance and satisfies the state constraints of each CAV under different communication topologies through an optimization problem. The second layer utilizes controller-matching technology to ensure the asymptotic stability of vehicle platoons and dissipate traffic waves. With the above double-layer structure, the DL-DMPC can simultaneously address multiple tasks and is applicable in various communication topologies. Simulations and analyses based on the NGSIM dataset are conducted in various scenarios to validate the effectiveness of the developed approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9326-9340"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536500","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}

{"title":"Learning an Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following","authors":"Ran Wei;Alfredo Garcia;Anthony McDonald;Gustav Markkula;Johan Engstrom;Matthew O'Kelly","doi":"10.1109/TITS.2025.3574552","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574552","url":null,"abstract":"In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstrations. The model’s structure consists of (i) the agent’s internal representation of how the environment and associated observations evolve as a result of control actions and (ii) the agent’s preferences over observable outcomes. We consider a model’s structure specification consistent with active inference, a theory of human perception and behavior from cognitive science. According to active inference, the agent acts upon the world so as to minimize surprise defined as a measure of the extent to which an agent’s current sensory observations differ from its preferred sensory observations. We propose a bi-level optimization approach to estimation which relies on a structural assumption on prior distributions that parameterize the statistical accuracy of the human agent’s model of the environment. To illustrate the proposed methodology, we present the estimation of a model for car-following behavior based upon a naturalistic dataset. Overall, the results indicate that learning active inference models of human perception and control from data is a promising alternative to closed-box models of driving.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9475-9490"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536295","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}

{"title":"Joint Optimization of Transit Network Design, Timetable, and Passenger Assignment With Exact Transfer Behavior Modeling","authors":"Yunyi Liang;Constantinos Antoniou;Mohammad Sadrani;Jinjun Tang","doi":"10.1109/TITS.2025.3573284","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573284","url":null,"abstract":"This study investigates the problem of joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling. The problem is formulated as a bi-level mixed-integer bilinear program to capture passengers’ realistic path choice behavior. The upper-level model aims to minimize the weighted sum of the cost of bus route construction, bus route operation, bus station construction, travel time of passengers, the delay caused by failures in aboarding to the bus trips at the origin, the delay caused by failures in transfer between the bus trips, and the overflow delay when the bus trip operates at capacity. The lower-level model aims to minimize the travel time of passengers. The travel time of passengers is formulated as the sum of the waiting time for boarding, the transfer time, and the in-vehicle travel time. The passenger transfer time and the delay caused by failures in transfer between the bus trips are formulated with exact modeling of passenger transfer behavior. This bi-level mixed-integer bilinear program is transformed into an equivalent mixed-integer bilinear program with equilibrium constraints using Karush-Kuhn-Tucker conditions. To seek a solution of good quality to the proposed model while not requiring a large amount of computer memory, a Benders decomposition algorithm integrated with piecewise linearization is developed. A numerical application demonstrates that the proposed model is able to achieve 3.49% lower total cost than the baseline model assuming passenger transfer time to be half of the headway.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9263-9276"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536552","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}

{"title":"Vehicle Longitudinal Stochastic Control for Connected and Automated Vehicle Platooning in Highway Systems","authors":"Min Dai;Chaozhong Wu;Jianghui Wen","doi":"10.1109/TITS.2025.3572555","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572555","url":null,"abstract":"The vehicle platoon control for highway traffic can help to improve traffic flow efficiency, enhance traffic safety, and reduce fuel consumption. In previous platoon control research, most of the driving behaviors are described using deterministic car-following models. Nevertheless, random factors that may come from the vehicle power train and additional stimuli, can have a greater impact on platoon control in highway traffic. In this paper, a novel stochastic control method is proposed based on a stochastic car-following model which considers microscopic driving behavior. Firstly, the stochastic car-following model is designed that fully accounts for the impact of random factors on platoon control. Secondly, an optimal objective function is constructed and the Hamilton-Jacobi-Bellman equation is used to solve this stochastic control problem, thereby completing the upper-level controller design and obtaining the optimal desired acceleration of the vehicle. Thirdly, the stochastic stability method is applied to analyze the proposed model and obtain the stochastic stability conditions satisfied by the model. Finally, tests are conducted for three different scenarios: stable speed, acceleration, and deceleration with additive and multiplicative noise, as well as the case where the lead vehicle’s speed is based on real vehicle trajectory data. These tests validate the stability and effectiveness of the stochastic car-following model predictive control method from the perspective of control strategy and model respectively. The experimental results show that under the stable parameter conditions of the model, the connected and automated vehicle platoon can achieve accurate speed tracking and maintain an appropriate safe distance in highway system.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9563-9578"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536292","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}

{"title":"HiMo: End-to-End Congestion Control for High Speed Rail Data Networking","authors":"Chenren Xu;Yuhan Zhou;Jing Wang;Ruihan Li;Lingyang Song;Guangyu Zhu","doi":"10.1109/TITS.2025.3558776","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558776","url":null,"abstract":"The highly variable nature of cellular networks challenges end-to-end network transmissions in achieving low-latency and high-throughput performance. In high-speed rail (HSR) networks, the intermittent connectivity and capacity dynamics imposed by high client mobility further add complexity and difficulty in providing seamless service. While congestion control algorithms (CCAs) play an essential role in ensuring optimal network performance, prior works on congestion control have predominantly concentrated on enhancing network performance within stationary or low-mobility mobile networks without considering frequent disconnections and highly dynamic network capacities imposed by HSR networks, resulting in severe RTT inflation and slow loss recovery. In this paper, we argue that a dedicated transport layer protocol is necessary for high-mobility scenarios. We propose an end-to-end low-latency congestion control algorithm HiMo for HSR networks that reacts to abrupt bandwidth changes quickly, handles frequent handovers, and is immediately deployable. Our trace-driven emulation on real-world datasets demonstrates that HiMo can reduce 51.3% 95th-percentile latency with comparable throughput on high-speed rail networks, compared to state-of-the-art CCAs.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7715-7725"},"PeriodicalIF":7.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243777","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}

{"title":"A Multi-Environment Freespace Detection Method Based on Range Scale Map","authors":"Siyuan Shao;Kunyang Wu;Genyuan Xing;Yang Liu;Guanyu Zhang","doi":"10.1109/TITS.2025.3568165","DOIUrl":"https://doi.org/10.1109/TITS.2025.3568165","url":null,"abstract":"Accurately freespace detection is crucial to ensure the safe operation of autonomous vehicles. However, creating multi-scene datasets can be challenging. Mainstream research primarily addresses driving scenes in urban settings while neglecting other types of road environments. This results in a constrained application environment for current freespace detection methods. This paper proposes an adaptive environment scale freespace detection method in 2D image space. The method does not require data labeling and has better environmental adaptability. The core idea is to adaptively map a fixed point cloud scale in 3D space to a pixel scale in 2D space using the camera projection relation to obtain the fine environmental gradient. Then design search rule to label freespace in 2D space. Experiments on two public datasets, urban and field, achieved F1 scores of 92.50% and 89.09%, respectively. In both structured and unstructured environments, the proposed method demonstrated higher accuracy and lower false detection rates compared to state-of-the-art methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9593-9608"},"PeriodicalIF":7.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536698","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}

{"title":"DBKE: Design of Blockchain-Envisioned Vehicle-to-Vehicle Secure Key Management Protocol Using ECC","authors":"Sanjeev Kumar Dwivedi;Ruhul Amin;Muhammad Khurram Khan;Ashok Kumar Das;Adesh Pandey;Md Abdul Saifulla","doi":"10.1109/TITS.2025.3572305","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572305","url":null,"abstract":"Vehicle-to-vehicle (V2V) authentication is essential in the Vehicular Ad-hoc Network (VANET). V2V communication improves the safety of drivers and assists them in making the appropriate decision according to road conditions. Since V2V communication happens in open public channels, an adversary takes advantage of it and tries to launch several potential threats. This article designs a blockchain-assisted V2V communication and authentication scheme using Elliptic Curve Cryptography (ECC) and a Physically Unclonable Function (PUF) called DBKE. In DBKE, vehicles perform their registration with their nearest Roadside Unit (RSU) without assisting the centralized cloud server. Then, one vehicle can communicate with another using the information stored in the blockchain. During the communication, both vehicles first perform the process of mutual authentication and then securely generate the session keys without sending the private parameters to the public channels. The formal analysis of DBKE is done with the Scyther and Real-or-Random (ROR) models, which confirm that the DBKE is robust and safe from attacks. Furthermore, the detailed comparative study of the security features reveals that the DBKE scheme provides superior security and low computation cost compared to the existing V2V authentication protocols.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9293-9304"},"PeriodicalIF":7.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536601","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}

{"title":"A Review of Computer Vision for Railways","authors":"Bryan Olivier;Feng Guo;Yu Qian;David P. Connolly","doi":"10.1109/TITS.2025.3552011","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552011","url":null,"abstract":"Modern railways continue to strive for remote and automated methods to improve the visual inspection procedures for their assets. In some cases, these inspections provide new information that could not previously be collected, while in other cases they help them improve upon the quality control, safety, time and costs associated with manual inspection. As such, computer vision continues to find applications for visually inspecting the track, earthworks, tunnels, overhead line equipment and rolling stock. Considering the recent pace of computer vision related developments, this paper seeks to review the state of the art of the field for railways. First, the hardware and data requirements are discussed, focusing on the unique challenges associated with operating optical equipment in a railway environment, such as contamination, power sources and lighting. This also discusses the most common mounting arrangements for camera hardware, including rolling-stock, satellites and way-side cameras. Next, image processing algorithms are discussed, comparing classical approaches and more modern artificial intelligence approaches, for example You Only Look Once (YOLO) and Region-Based Convolutional Neural Network (R-CNN). Then the most common applications for computer vision in the rail industry are analysed. First the track is studied considering computer vision analysis for the detection of different types of rail surface defects on plain line and turnouts, fastener defects, concrete track slab cracking and ballast particle characterisation. Next, the overhead line equipment is considered with applications related to detecting contact loss between pantograph and contact wire, stagger behaviour and defective catenary components. This is followed by discussion of other applications such as rail tunnelling subsidence, tunnel inspection, level crossings, trespass and on-track safety hazards. Finally, opportunities for future research are discussed such as hyperspectral imaging and generative AI, along with possible frontier technologies such as quantum computing.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"11034-11065"},"PeriodicalIF":7.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536423","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}

{"title":"Transportation Analytics Using Smart Card Data: A Systematic Review","authors":"Leonardo Monteiro-Fialho;Enrique Cueto-Rubio;Carlos Granell;Sergio Trilles","doi":"10.1109/TITS.2025.3571101","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571101","url":null,"abstract":"The widespread adoption of smart card technology in mobility services, particularly in public transportation, as well as in car and bike sharing systems, has opened up new avenues for transportation service providers to gain insights into passenger travel behaviors. Smart card data offers a rich source of information on passenger trips, enabling a wide range of transportation analytics applications, including operator performance monitoring, demand modeling, and travel behavior analysis. This systematic mapping review aims to comprehensively examine the current state-of-the-art in leveraging smart cards for analytical studies applied to public transportation research. The review focuses on identifying and analyzing the main analytical purposes, methods, techniques, datasets, and trends used in these studies. The findings of this review provide valuable insights into the current research landscape of smart card data for public transport and highlight potential knowledge gaps that warrant further research.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"11010-11033"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536396","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}