IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Anytime Optimal Trajectory Repairing for Autonomous Vehicles","authors":"Kailin Tong;Martin Steinberger;Martin Horn;Selim Solmaz;Daniel Watzenig","doi":"10.1109/OJITS.2025.3563823","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563823","url":null,"abstract":"Adapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address this challenge by replanning the entire trajectory, a significantly more efficient strategy is to repair only the flawed segments. Our paper introduces a groundbreaking approach by formulating an optimal trajectory repairing problem and proposing an innovative and efficient framework for critical timing detection and trajectory repairing. This trajectory repairing specifically employs Bernstein basis polynomials in both 2D distance-time and 3D spatiotemporal spaces. A distinctive feature of our method is the use of an anytime grid search to determine a sub-optimal time-to-repair, which contrasts with previous methods that relied on manually tuned or fixed repair times, limiting both flexibility and robustness. A statistical analysis of 100 scenarios demonstrates that our trajectory-repairing framework outperforms the path-speed decoupled repairing framework in terms of scenario success rate. Furthermore, we introduce a novel algorithm for driving corridor generation that more accurately approximates the collision-free space than state-of-the-art work. The proposed approach has broad potential for application in embedded systems across various autonomous platforms.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"537-553"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain Adaptation for Vehicle Detection Under Adverse Weather","authors":"Huei-Yung Lin;Yi-Chao Huang;Jing-Xian Lai;Ting-Ting You","doi":"10.1109/OJITS.2025.3563373","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563373","url":null,"abstract":"The images captured under varying illumination or adverse weather conditions exhibit distinct distributions in the high-dimensional feature space, hindering the performance of object detection networks. To address this issue, we propose a domain adaptation method based on adversarial learning. This approach ensures that extracted features have a similar distribution, even when input images originate from different data acquisition domains. Due to the lack of driving images recorded under a variety of weather conditions in existing datasets, we incorporate a semi-supervised learning framework to enhance detection performance by training with unlabeled images. Experimental results on public and our latest datasets demonstrate that the proposed adversarial learning technique surpasses recent traffic scene object detection networks across various driving scenarios. Code and datasets are available at <uri>https://github.com/daniel851218/all-weather-vehicle-detector</uri>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"568-578"},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OppIN: Optimal Path Intervention for Emergency Response Leveraging IoT and Big Data Technologies","authors":"Yassine Gacha;Takoua Abdellatif","doi":"10.1109/OJITS.2025.3563310","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3563310","url":null,"abstract":"In this paper, we introduce the Optimal Path Intervention System (OppIN), a solution designed to support multiple emergency services, including fire response, civil protection, and emergency medical assistance, to reach crisis locations as quickly as possible by harnessing Big Data technologies and IoT infrastructure. OppIN computes quasi-real-time optimal intervention paths using a multi-criteria approach, incorporating both static factors (such as road network geometry, road conditions, and service locations) and dynamic data (including crisis locations captured by IoT sensors and real-time traffic conditions monitored through surveillance cameras). Using the IoT infrastructure and local data for quasi-real-time updates, OppIN adapts effectively to dynamic changes in context, ensuring the use of up-to-date information alongside Big Data technologies and AI for real-time processing. Compared to existing solutions such as Google Maps, our system uses a broader set of data sources and criteria, such as weather conditions, distance, traffic dynamics, and road status, to provide a more comprehensive and tailored analysis for specialized service navigation. Additionally, OppIN offers superior scalability and performance, using a Big Data-driven system design to handle high data volumes and real-time processing demands effectively. Furthermore, our system uses AI programs to estimate different criteria and to aggregate these criteria for quasi-real-time paths calculation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"484-502"},"PeriodicalIF":4.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Hop Upstream Anticipatory Traffic Signal Control With Deep Reinforcement Learning","authors":"Xiaocan Li;Xiaoyu Wang;Ilia Smirnov;Scott Sanner;Baher Abdulhai","doi":"10.1109/OJITS.2025.3562757","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3562757","url":null,"abstract":"Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. However, effective signal control inherently requires coordination across a broader spatial scope, as the effect of upstream traffic should influence signal control decisions at downstream intersections, impacting a large area in the traffic network. Although agent communication using neural network-based feature extraction can implicitly enhance spatial awareness, it significantly increases the learning complexity, adding an additional layer of difficulty to the challenging task of control in deep reinforcement learning. To address the issue of learning complexity and myopic traffic pressure definition, our work introduces a novel concept based on Markov chain theory, namely multi-hop upstream pressure, which generalizes the conventional pressure to account for traffic conditions beyond the immediate upstream links. This farsighted and compact metric informs the deep reinforcement learning agent to preemptively clear the multi-hop upstream queues, guiding the agent to optimize signal timings with a broader spatial awareness. Simulations on synthetic and realistic (Toronto) scenarios demonstrate controllers utilizing multi-hop upstream pressure significantly reduce overall network delay by prioritizing traffic movements based on a broader understanding of upstream congestion.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"554-567"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twin Technologies for Vehicular Prototyping: A Survey","authors":"Md Rafiul Kabir;Bhagawat Baanav Yedla Ravi;Sandip Ray","doi":"10.1109/OJITS.2025.3562504","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3562504","url":null,"abstract":"Digital Twin (DT) technology is widely regarded as one of the most promising tools for industry development, demonstrating substantial application across numerous cyber-physical systems. Gradually, this technology has been introduced into modern vehicular systems focusing on its application in intelligent driving, connected vehicles, automotive engineering, aircraft health, and many more. By creating dynamic, virtual replicas of physical vehicles and their associated components, DT enables unprecedented levels of analysis, simulation, and real-time monitoring, thereby enhancing performance, safety, and sustainability. This paper offers a comprehensive review, extending beyond digital twins to include various prototyping approaches for target-specific applications focusing on the smart vehicular systems across automotive, aviation, and maritime domains driving the evolution of next-generation vehicular infrastructure.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"503-521"},"PeriodicalIF":4.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Optimization of Transportation-Energy Systems Through Electric Vehicle Charging Pricing in the Morning Commute","authors":"Kevin Freymiller;Junjie Qin;Sean Qian","doi":"10.1109/OJITS.2025.3557038","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3557038","url":null,"abstract":"We investigate how electric vehicles (EV) market share and EV charging pricing would impact the joint transportation and grid system during the morning commute. Using a simplified network consisting of a single corridor, we analytically derive time-varying flow patterns for both EV and internal combustion engine vehicle (ICV) groups, as a result of travelers’ departure time choices upon travel time, schedule delay and EV charging fee at an arbitrary morning time. For cities with a small or moderate portion of electricity generated from solar, one primary cost for the grid system during the morning commute is power generation ramping in addition to energy cost. By imposing a single charging price change during the morning commute period, we solve for the optimal charging price change time and magnitude to minimize joint system cost. We show that a price increase during morning commute is always preferred. There is a trade-off between transportation and grid costs with respect to when/how grid and transportation infrastructure are utilized by vehicles, particularly electric vehicles. Increasing EV peak charge would increase the grid ramping cost, as more EVs would depart home earlier. However, the same EV peak charge would reduce the transportation cost when the charge is mild or EV penetration is relatively low. When the energy generation ramping is considerable, there always exists an optimal EV peak charge balancing transportation cost and grid cost. We mathematically show the benefits of replacing ICVs with EVs in reducing transportation cost on top of emission/energy reductions, which can be achieved by imposing optimal EV charging prices alone. In addition, we would impose a higher peak charging price during winter for high latitude areas, or areas on the western end of a time zone, as such a price would reduce transportation cost without burdening the grid.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"465-483"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features","authors":"Ping-Tzu Lin;Ying-Shiuan Huang;Wen-Chieh Lin;Chieh-Chih Wang;Huei-Yung Lin","doi":"10.1109/OJITS.2025.3555574","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3555574","url":null,"abstract":"Autonomous vehicles have gained great attention from all walks of life in recent years. The relative position and orientation between sensors often change gradually over time due to vibrations or thermal stress of materials. Thus, online re-calibrating extrinsic parameters periodically is required. In this situation, automatic targetless methods are more preferable as they do not require a calibration target or tedious calibration procedure. In this paper, we propose an online targetless camera-LiDAR extrinsic calibration approach with the help of semantic information. Our method could effectively ameliorate the problem of targetless methods which usually lack robust features and the correspondences. We also propose a feature selection technique to filter out improper feature correspondences by matching the image contours and point cloud projection contours. The experiment results show that our approach is more robust than previous work, and the calibration algorithm is applicable to more scenarios.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"456-464"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Logic-Enhanced Sustainable and Resilient EV Public Transit Systems for Rural Tourism","authors":"Rapeepan Pitakaso;Thanatkij Srichok;Surajet Khonjun;Peerawat Luesak;Chutchai Kaewta;Sarayut Gonwirat;Prem Enkvetchakul;Rerkchai Srivoramas","doi":"10.1109/OJITS.2025.3554204","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554204","url":null,"abstract":"The integration of electric vehicles (EVs) into public transit systems is crucial for enhancing sustainability and operational efficiency, particularly in rural tourism regions where demand is highly variable and infrastructure constraints pose unique challenges. Traditional transportation planning approaches often lack the adaptability required to handle the fluctuating nature of tourist mobility, leading to inefficiencies in service coverage and resource utilization. While fuzzy logic-based models have been extensively applied in urban transit optimization, their applicability to rural EV public transit remains underexplored. This study addresses this gap by developing the Fuzzy-Artificial Multiple Intelligence System (F-AMIS), an enhanced version of the Artificial Multiple Intelligence System (AMIS). F-AMIS integrates new intelligence boxes and an optimized selection formula, allowing for real-time adaptive decision-making in EV bus networks. A real-world case study demonstrates that F-AMIS significantly outperforms conventional optimization methods, achieving a 20% reduction in operational costs and increasing service coverage from 75% to 90%, while also enhancing resilience and sustainability indices. These findings highlight the potential of F-AMIS as a scalable, intelligent optimization framework for improving the efficiency and sustainability of rural EV transit systems. Future research should explore integrating F-AMIS with advanced AI-driven decision models, refining fuzzy logic techniques for rural-specific constraints, and assessing the model’s adaptability across diverse global tourism networks to further enhance its applicability and impact.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"407-432"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey","authors":"Victor M. G. Martinez;Divanilson R. Campelo;Moises R. N. Ribeiro","doi":"10.1109/OJITS.2025.3553696","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3553696","url":null,"abstract":"Intelligent transportation systems (ITS) have been attracting the attention of industry and academia alike for addressing issues raised by the 2030 agenda for sustainable development goals (SDG) approved by the United Nations. However, the diversity and dynamics of present-day transportation scenarios are already very complex, turning the management of ITS into a virtually impossible task for conventional traffic control centers. Recently, the digital twin (DT) paradigm has been presented as a modern architectural concept to tackle complex problems, such as the ones faced by ITS. This survey aims to provide a piece-wise approach to introducing DTs into sustainable ITS by addressing the following cornerstone aspects: i) Why should one consider DTs in ITS applications? ii) What can DTs represent from ITS’ new physical environments? And iii) How can one use DTs to address ITS SDG related to efficiency, safety, and ecology? Our methodological approach for surveying the literature addresses these questions by categorizing contributions and discriminating their ITS elements and agents against the SDG they addressed. Thus, this survey provides an in-depth and contextualized overview of the challenges when approaching ITS through DTs, including scenarios involving autonomous and connected vehicles, ITS infrastructure, and traffic agents’ behavior. Moreover, we propose a functional reference framework for developing DTs of ITS. Finally, we also offer research challenges regarding standardization, connectivity infrastructure, security and privacy aspects, and business management for properly developing DTs for sustainable ITS.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"363-392"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and Simulation of Automotive FMCW RADAR Sensor for Environmental Perception","authors":"Arsalan Haider;Abdulkadir Eryildirim;Marcell Pigniczki;Lukas Haas;Birgit Schlager;Thomas Zeh;David Nickel;Alexander W. Koch","doi":"10.1109/OJITS.2025.3554452","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554452","url":null,"abstract":"Frequency-modulated continuous wave (FMCW) radio detection and ranging (RADAR) sensors have become indispensable technologies for automated driving systems (ADS) due to their reliability in adverse weather conditions and their ability to simultaneously measure the distance to objects, relative radial velocity, and azimuth and elevation angles. The automotive industry has increasingly considered simulation-based testing of autonomous vehicles due to safety, cost, and time constraints. This raises the need for virtual environmental perception sensors that provide results close to reality. This work presents the design and structure of a ray-tracing-based, high-fidelity, tool-independent baseband FMCW RADAR sensor model. The RADAR sensor model is developed using the standardized functional mock-up interface (FMI) and open simulation interface (OSI) and is integrated into the co-simulation environment of commercial software to demonstrate its exchangeability. The RADAR FMU model incorporates a multiple input and multiple output (MIMO) 2D linear spacing virtual antenna array, non-coherent integration (NCI) of range-Doppler maps (RDMs) over receiver antennas, a constant false alarm rate (CFAR) to obtain an interim object detection list, and density-based spatial clustering of applications with noise (DBSCAN) to provide a single detection per object. The presented RADAR FMU model also includes RADAR sensor-specific impairments such as phase noise (PN), radio frequency (RF) group delay, phase imbalance (PI) of transmitter antennas, mixer non-linearity including third-order intermodulation products (IM3), and noise figure (NF) of receiver antennas. Additionally, this work presents a methodology for plausibly verifying the RADAR sensor model at the raw data level (range map (RM) and RDM) and object detection list level. The simulation results are compared with real sensor measurements to validate the modeling of sensor-specific impairments. The mean absolute percentage error (MAPE) metric is used to quantify the difference between the simulation and real sensor measurements. The results demonstrate that the complete signal processing toolchain and sensor-specific impairments of the RADAR sensor must be considered to achieve simulation results that closely resemble those of the real sensor.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"433-455"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}