IEEE Open Journal of Intelligent Transportation Systems最新文献_第2页

Evaluating the Synergy of Conflict Detection and Resolution Services for Constrained Urban Airspace

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-16 DOI: 10.1109/OJITS.2025.3530516

Călin Andrei Badea;Andrija Vidosavljević;Joost Ellerbroek;Jacco Hoekstra

{"title":"Evaluating the Synergy of Conflict Detection and Resolution Services for Constrained Urban Airspace","authors":"Călin Andrei Badea;Andrija Vidosavljević;Joost Ellerbroek;Jacco Hoekstra","doi":"10.1109/OJITS.2025.3530516","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3530516","url":null,"abstract":"Very-low-level (VLL) urban air operations have been extensively investigated as a solution for mitigating congestion in cities. However, the manner in which the management of such traffic should be performed is still actively investigated. One important component of such a system is the conflict detection and resolution (CD&R), mainly composed of the strategic and tactical CD&R module. While many approaches towards these have been studied, insufficient analysis has been conducted on their compatibility when functioning within a unified, hybrid system. Additionally, their robustness to operational uncertainties such as wind and departure delays is often overlooked. In this work, we investigate the performance of strategic planing methods when combined with tactical CD&R and subjected to a wide range of traffic demand levels and uncertainty conditions. Simulations indicate that the performance of the strategic deconfliction module is highly sensitive to the presence of wind and delay. This decline in performance is partially mitigated by the tactical deconfliction module. Thus, the results suggest that increased use of tactical CD&R could lessen the required level of detail of strategic deconfliction methods, leading to improved compatibility between the two modules.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"24-36"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843729","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107049","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}

引用次数: 0

Risk-Aware Stochastic Vehicle Trajectory Prediction With Spatial-Temporal Interaction Modeling

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-01-16 DOI: 10.1109/OJITS.2025.3530268

Yuxiang Feng;Qiming Ye;Eduardo Candela;Jose Javier Escribano-Macias;Bo Hu;Yiannis Demiris;Panagiotis Angeloudis

{"title":"Risk-Aware Stochastic Vehicle Trajectory Prediction With Spatial-Temporal Interaction Modeling","authors":"Yuxiang Feng;Qiming Ye;Eduardo Candela;Jose Javier Escribano-Macias;Bo Hu;Yiannis Demiris;Panagiotis Angeloudis","doi":"10.1109/OJITS.2025.3530268","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3530268","url":null,"abstract":"Autonomous vehicles need to continuously analyse the driving context and establish a comprehensive understanding of the dynamic traffic environment. To ensure the safety and efficiency of their operations, it would be beneficial to have accurate predictions of surrounding vehicles’ future trajectories. AVs can adjust their motions proactively to improve road safety and comfort with such information. This paper proposes a novel approach to predict the future trajectories of interacting vehicles, through a model of potential spatial-temporal interactions. A unique kernel function that emphasises risk-awareness was developed to extract spatial dependencies. The established model was trained and evaluated with the publicly available Highway Drone Dataset and Intersection Drone Dataset. The performance of the developed model was assessed with eight state-of-the-art methods. An ablation study and safety analysis were also conducted to evaluate the proposed risk-awareness kernel function. Results show that the proposed model’s inference speed is over eight times faster than the commonly used LSTM-based models. It also achieves an improvement of over 8% in prediction accuracy when compared with the state-of-the-art model.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"37-48"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107051","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}

引用次数: 0

Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach 自适应巡航控制车辆的细微网络攻击检测：一种机器学习方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-12-31 DOI: 10.1109/OJITS.2024.3522969

Tianyi Li;Mingfeng Shang;Shian Wang;Raphael Stern

{"title":"Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach","authors":"Tianyi Li;Mingfeng Shang;Shian Wang;Raphael Stern","doi":"10.1109/OJITS.2024.3522969","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3522969","url":null,"abstract":"With the emergence of vehicles featuring advanced driver-assistance systems like adaptive cruise control (ACC) and additional automated driving functionalities, there has arisen a heightened potential for cyberattacks targeting these automated vehicles (AVs). While overt attacks that lead to collisions are more conspicuous, subtle attacks that slightly modify driving behaviors can cause widespread impacts, including increased congestion, fuel consumption, and crash risks without being easily detected. To address the detection of such attacks, we first present a traffic modeling framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, data poison attacks, and denial-of-service (DoS) attacks. Subsequently, we examine the consequences of these attacks on both singular vehicle dynamics (micro) and broader traffic flow patterns (macro). We introduce a new anomaly detection model based on generative adversarial networks (GAN) designed for the real-time pinpointing of such attacks using vehicle trajectory data. Numerical results are presented to show the effectiveness of our machine learning strategy in identifying cyberattacks on vehicles equipped with ACC. The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"11-23"},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993036","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}

引用次数: 0

Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy 过渡网格地图：静态和动态占用的联合建模

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-12-25 DOI: 10.1109/OJITS.2024.3521449

José Manuel Gaspar Sánchez;Leonard Bruns;Jana Tumova;Patric Jensfelt;Martin Törngren

引用次数: 0

DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning 整合动态博弈论和深度学习的轨迹规划

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-12-11 DOI: 10.1109/OJITS.2024.3515270

Giovanni Lucente;Mikkel Skov Maarssoe;Sanath Himasekhar Konthala;Anas Abulehia;Reza Dariani;Julian Schindler

{"title":"DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning","authors":"Giovanni Lucente;Mikkel Skov Maarssoe;Sanath Himasekhar Konthala;Anas Abulehia;Reza Dariani;Julian Schindler","doi":"10.1109/OJITS.2024.3515270","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3515270","url":null,"abstract":"Trajectory planning for automated vehicles in traffic has been a challenging task and a hot topic in recent research. The need for flexibility, transparency, interpretability and predictability poses challenges in deploying data-driven approaches in this safety-critical application. This paper proposes DeepGame-TP, a game-theoretical trajectory planner that uses deep learning to model each agent’s cost function and adjust it based on observed behavior. In particular, a LSTM network predicts each agent’s desired speed, forming a penalizing term that reflects aggressiveness in the cost function. Experiments demonstrated significant advantages of this innovative framework, highlighting the adaptability of DeepGame-TP in intersection, overtaking, car following and merging scenarios. It effectively avoids dangerous situations that could arise from incorrect cost function estimates. The approach is suitable for real-time applications, solving the Generalized Nash Equilibrium Problem (GNEP) in scenarios with up to four vehicles in under 100 milliseconds on average.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"873-888"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10793110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925364","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}

引用次数: 0

A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation 救护车服务中的机器学习创新调查：分配、路由和需求估计

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-12-11 DOI: 10.1109/OJITS.2024.3514871

Reem Tluli;Ahmed Badawy;Saeed Salem;Mahmoud Barhamgi;Amr Mohamed

{"title":"A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation","authors":"Reem Tluli;Ahmed Badawy;Saeed Salem;Mahmoud Barhamgi;Amr Mohamed","doi":"10.1109/OJITS.2024.3514871","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3514871","url":null,"abstract":"In the realm of Emergency Medical Services (EMS), the integration of Machine Learning (ML) techniques has emerged as a catalyst for revolutionizing ambulance operations. ML algorithms could play a pivotal role in dynamically allocating resources, devising efficient routes, and predicting demand patterns. By thoroughly reviewing the existing literature and methodologies, this paper provides a comprehensive overview of the approaches used in ambulance allocation, routing, demand estimation and simulation models. We discuss the challenges faced by these methods, emphasizing the need for innovative solutions that can adapt to real-time data and changing emergency patterns. Through this survey, we aim to offer valuable insights into the current state of research and practices, shedding light on potential areas for future exploration and development. The findings presented in this paper serve as a foundation for researchers and practitioners working towards enhancing the efficiency of ambulance deployment in EMS.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"842-872"},"PeriodicalIF":4.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858865","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}

引用次数: 0

Macroscopic Fundamental Diagram: Alternative Theoretical Analysis and Implications for Traffic Control 宏观基本图：交通管制的理论分析与启示

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-12-09 DOI: 10.1109/OJITS.2024.3514536

Pushkin Kachroo;Shaurya Agarwal;Kaan Ozbay

{"title":"Macroscopic Fundamental Diagram: Alternative Theoretical Analysis and Implications for Traffic Control","authors":"Pushkin Kachroo;Shaurya Agarwal;Kaan Ozbay","doi":"10.1109/OJITS.2024.3514536","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3514536","url":null,"abstract":"This paper presents the theory and analysis related to the aggregated macroscopic fundamental diagram and presents specific implications for traffic control. The paper presents the aggregation results for the three fundamental variables, traffic density, speed, and traffic flow, and the relationship among the aggregated versions of these for the Greenshields’ model and a piecewise affine model. The development of the algebraic relationships is followed by stochastic analysis to obtain aggregation results in the limiting sense. The dynamics of the aggregated variables are studied, and the idea of dynamic region for aggregation in terms of dynamic reachability is utilized. We also provide an analysis of the error bounds that can be utilized during perimeter control design using MFDs. Finally, the implications of this new analysis are studied in terms of traffic control for an aggregated region, followed by traffic control simulations. Two separate control problems are formulated and studied, which include a) MFD-based control strategy on freeways and b) MFD-based modeling and control of urban sub-networks. Control methodologies used for the two problems include conservation law-based direct control design and feedback linearization control, respectively.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"826-841"},"PeriodicalIF":4.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858864","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}

引用次数: 0

Lane Changing Control of Autonomous Vehicle With Integrated Trajectory Planning Based on Stackelberg Game 基于Stackelberg博弈的自动驾驶汽车综合轨迹规划变道控制

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-11-29 DOI: 10.1109/OJITS.2024.3509462

Dongmei Wu;Zhen Li;Changqing Du;Changsheng Liu;Yang Li;Xin Xu

{"title":"Lane Changing Control of Autonomous Vehicle With Integrated Trajectory Planning Based on Stackelberg Game","authors":"Dongmei Wu;Zhen Li;Changqing Du;Changsheng Liu;Yang Li;Xin Xu","doi":"10.1109/OJITS.2024.3509462","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3509462","url":null,"abstract":"Lane changing present a significant challenge for autonomous vehicles, as they must maintain safe driving and optimize time efficiency. This process is strongly affected by traffic environment and driver characteristics. This paper proposed a lane changing control method based on Stackelberg game theory, integrating lane changing decision and trajectory planning while comprehensively considering the driver’s characteristics and the traffic environment. Firstly, considering the common characteristics of lane changing decision and trajectory planning, the two stages are integrated using the leader-follower game theory, enhancing the accuracy of lane changing decisions. Secondly, the cooperative game theory model is employed to design an adaptive weight adjustment strategy for the trajectory tracking controller. The weight coefficients for vehicle stability and path tracking accuracy are dynamically adjusted within the model predictive control method to adapt to the vehicle’s stability state. Simulation results indicate a 24% improvement in decision-making accuracy with the proposed leader-follower game decision method over the rule-based lane changing model. The average relative error in lateral displacement, comparing the vehicle’s actual trajectory to the planned one, is reduced by 6%. Additionally, the variable-weight trajectory tracking control enhances overall tracking performance by over 30% in scenarios involving high speeds and low adhesion. These findings verify the proposed vehicle lane changing method notably improves lane changing safety, stability, and precision.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"810-825"},"PeriodicalIF":4.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858868","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}

引用次数: 0

Realtime Multispectral Pedestrian Detection With Visible and Far-Infrared Under Ambient Temperature Changing 环境温度变化下可见光和远红外实时多光谱行人检测

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-11-27 DOI: 10.1109/OJITS.2024.3507917

Masato Okuda;Kota Yoshida;Takeshi Fujino

{"title":"Realtime Multispectral Pedestrian Detection With Visible and Far-Infrared Under Ambient Temperature Changing","authors":"Masato Okuda;Kota Yoshida;Takeshi Fujino","doi":"10.1109/OJITS.2024.3507917","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3507917","url":null,"abstract":"In recent intelligent transportation systems (ITS), it is important to recognize pedestrians and avoid collisions. Various sensors are used to detect pedestrians, and some research on pedestrian detection uses a visible light (RGB) camera and a far-infrared (FIR) camera. FIR cameras are significantly affected by ambient temperatures such as summer and winter. However, few studies have focused on this property when evaluating pedestrian detection accuracy. Therefore, this paper investigates the effect of temperature change in real-time multispectral pedestrian detection. We created an original dataset with three subsets, Hot, Intermediate, and Cold, and evaluated temperature effects by changing the subsets during training and testing. We first evaluated YOLOv8s-4ch, which simply extended the input layer of YOLOv8 from 3 channels of RGB to 4 channels of RGB-FIR. To further improve detection performance, we built a new model called YOLOv8s-2stream. This model has two backbones for RGB and FIR, and fuses their feature maps in each resolution. We found that the model trained on a specific temperature subset dropped the test accuracy in other subsets. On the other hand, when training using a Mix set covering all temperature sets (Hot, Inter., Cold), the model achieved the highest accuracy through all conditions. Moreover, our YOLOv8s-2stream has improved by 3.9 points of accuracy (AP@0.5:0.95) compared to YOLOv8s-4ch, and achieved 73 FPS inference speed on Jetson.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"797-809"},"PeriodicalIF":4.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844534","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}

引用次数: 0

Predictor-Based CACC Design for Heterogeneous Vehicles With Distinct Input Delays 基于预测器的 CACC 设计，适用于具有不同输入延迟的异构车辆

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-11-07 DOI: 10.1109/OJITS.2024.3493461

Amirhossein Samii;Nikolaos Bekiaris-Liberis

{"title":"Predictor-Based CACC Design for Heterogeneous Vehicles With Distinct Input Delays","authors":"Amirhossein Samii;Nikolaos Bekiaris-Liberis","doi":"10.1109/OJITS.2024.3493461","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3493461","url":null,"abstract":"We develop a predictor-based cooperative adaptive cruise control (CACC) design for platoons with heterogeneous vehicles, whose dynamics are described by a third-order linear system subject to actuators delays, which are distinct for each individual vehicle. The design achieves individual vehicle stability, string stability, and zero, steady-state speed/spacing tracking errors, relying on a nominal, constant time headway (CTH)-type CACC design that achieves these specifications when all actuators’ delays are zero. This is achieved owing to the delay-compensating mechanism, of the CACC law introduced, for long delays and despite the fact that each vehicle’s dynamics are subject to different input delays, which makes the available predictor-feedback CACC designs inapplicable. The proofs of individual vehicle stability, string stability, and regulation rely on employment of an input-output approach on the frequency domain. We present consistent simulation results, including an example in which we employ real traffic data for the trajectory of the leading vehicle and an example via which we compare the performance of our design with the existing, predictor-feedback CACC and predictor-based ACC laws. In addition, we study numerically the robustness properties with respect to string stability of our predictor-based CACC design to (uncertain) communication delays. Thus, our numerical results validate the performance of the design in realistic scenarios and as compared with related, existing control laws.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"783-796"},"PeriodicalIF":4.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713821","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}

引用次数: 0