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IEEE Open Journal of Intelligent Transportation Systems
Volume 6: 2025
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IEEE OPEN JOURNAL OF THE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY
Publication Year: 2025,Page(s):C2 - C2
IEEE OPEN JOURNAL OF THE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY
Year: 2025 | Volume: 6
Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy
José Manuel Gaspar Sánchez;Leonard Bruns;Jana Tumova;Patric Jensfelt;Martin Törngren
Publication Year: 2025,Page(s):1 - 10
Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environm...Show More
Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach
Publication Year: 2025,Page(s):11 - 23
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...Show More
Evaluating the Synergy of Conflict Detection and Resolution Services for Constrained Urban Airspace
Călin Andrei Badea;Andrija Vidosavljević;Joost Ellerbroek;Jacco Hoekstra
Publication Year: 2025,Page(s):24 - 36
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 ...Show More
Safety-oriented Perception Systems for Intelligent Vehicles and Transportation Systems
Risk-Aware Stochastic Vehicle Trajectory Prediction With Spatial-Temporal Interaction Modeling
Yuxiang Feng;Qiming Ye;Eduardo Candela;Jose Javier Escribano-Macias;Bo Hu;Yiannis Demiris;Panagiotis Angeloudis
Publication Year: 2025,Page(s):37 - 48
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 informatio...Show More
Autonomous and Cooperative Driving in Specific Traffic Environments
Cooperative Localization of Multi-Agent Autonomous Aerial Vehicle (AAV) Networks in Intelligent Transportation Systems
Publication Year: 2025,Page(s):49 - 66
GNSS-independent localization is one of the most prominent research problems in aerial autonomous systems navigation, especially in certain applications where Simultaneous Localization and Mapping (SLAM) methods are inapplicable due to the complexity of the environment, or in open-air spaces where a flock of Autonomous Aerial Vehicles (AAVs) navigate in a GNSS-independent fashion. This paper intro...Show More
Intelligent Transportation System Protocol Interoperability Evaluation
Publication Year: 2025,Page(s):67 - 94
For decades, systems in the mobility sector have been maintained as discrete entities. It is imperative that they collaborate in order to enhance traffic safety and efficiency, while simultaneously reducing environmental pollution. The protocols employed in the formally separated ecosystems demonstrate disparate behaviors and data representations. In order to facilitate connectivity between these ...Show More
Safety-Critical Oracles for Metamorphic Testing of Deep Learning LiDAR Point Cloud Object Detectors
Simon Speth;Maximilian Trien;Dominik Kufer;Alexander Pretschner
Publication Year: 2025,Page(s):95 - 108
Robustness testing is crucial for verifying autonomous vehicles, especially for safety-critical deep learning components like light detection and ranging (LiDAR) object detectors. Metamorphic testing (MT) assesses the robustness by automatically generating test cases based on abstract system specifications known as metamorphic relations (MRs). However, a key challenge is ensuring a traceable safet...Show More
Designing Directional Traffic Flow With Edge Mode Combination in 2-D Topological Structures
Publication Year: 2025,Page(s):109 - 115
We demonstrate directional vehicular traffic using a two-dimensional honeycomb-shaped topological structure. We consider a hexagonal street network modeled with vertices and edges, and numerically simulate vehicular transport as a symmetric random walk between vertices. We show that two topologically protected modes lead to traffic flows in orthogonal directions. Additionally, we introduce a synth...Show More
Connected and Automated mobility by road and rails: emerging methodologies and technologies
Freeway Traffic Modeling by Physics-Regularized Gaussian Processes
Publication Year: 2025,Page(s):116 - 130
Effective traffic management and control are essential for mitigating congestion and minimizing environmental impacts on road transportation systems. In this paper, we propose a novel approach for traffic modeling that integrates physics-based dynamics with machine learning techniques. Our method leverages Gaussian Processes (GPs) and a multi-class second-order discrete traffic model known as META...Show More
Experimental Demonstration of Platoon Formation Using a Cooperative Merging Controller
Wouter Scholte;Tom van der Sande;Peter Zegelaar;Henk Nijmeijer
Publication Year: 2025,Page(s):131 - 143
Road safety and traffic congestion are amongst the main challenges in current transportation systems. Literature shows that these challenges can be tackled using cooperative platoons, which is a technique in which vehicles use communication to drive closely behind each other in a string. An important topic of research regarding cooperative platoons is merging vehicles into a platoon at highway on-...Show More
Representation Learning for Place Recognition Using MIMO Radar
Publication Year: 2025,Page(s):144 - 154
Traditional radar perception often rely on point clouds derived from radar heatmap using CFAR filtering, which can result in the loss of valuable information, especially weaker signals crucial for accurate perception. To address this, we present a novel approach for representation learning directly from pre-CFAR heatmaps, specifically for place recognition using a high-resolution MIMO radar sensor...Show More
Vehicle Re-Identification and Tracking: Algorithmic Approach, Challenges and Future Directions
Ashutosh Holla B.;Manohara M. M. Pai;Ujjwal Verma;Radhika M. Pai
Publication Year: 2025,Page(s):155 - 183
Vehicle re-identification and tracking play a vital role in intelligent transportation systems as they enhance traffic management, improve safety, and optimize flow by precisely monitoring and analyzing vehicle movements across various locations. This technology enables the collecting of data in real-time, which allows for effective identification of incidents, enforcement of laws, and decision-ma...Show More
An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations
Nikos Kougiatsos;Evelien L. Scheffers;Marcel C. van Benten;Dingena L. Schott;Peter de Vos;Rudy R. Negenborn;Vasso Reppa
Publication Year: 2025,Page(s):184 - 203
Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine v...Show More
Evaluation of CNN-Based Approaches to Adverse Weather Image Classification for Autonomous Driving Systems
Publication Year: 2025,Page(s):204 - 229
Weather image classification is a critical component of the vision systems in autonomous driving systems (ADSs), facilitating accurate decision-making across diverse driving conditions. Adverse weather conditions (AWCs) can significantly impair sensor data quality, diminishing the ADSs’ ability to interpret the surrounding environment. It is, therefore, essential for ADSs to effectively perceive a...Show More
An Adaptive Hierarchical Framework With Contrastive Aggregation for Traffic Sign Classification
Publication Year: 2025,Page(s):230 - 243
Autonomous vehicles rely on accurate traffic sign classification, which is typically achieved through supervised learning. However, the diversity and complexity of traffic signs make it impractical to rely solely on large labeled datasets. While abundant data exists for common signs such as stop and yield signs, less common signs often lack sufficient representation in existing datasets. Few-shot ...Show More
Vehicle Egomotion Estimation Through IMU-RADAR Tight-Coupling
Publication Year: 2025,Page(s):244 - 255
The current state-of-the-art vehicle egomotion state estimation systems are limited in their usage for advanced driver-assistance systems (ADAS), as the estimation accuracy is limited in driving scenarios where large amounts of wheel slip occur. Addressing the fundamental limitations of vehicle egomotion state estimation, this study investigates the usage of RADAR in vehicle egomotion state estima...Show More
Safety-oriented Perception Systems for Intelligent Vehicles and Transportation Systems
VI-BEV: Vehicle-Infrastructure Collaborative Perception for 3-D Object Detection on Bird’s-Eye View
Publication Year: 2025,Page(s):256 - 265
As infrastructure equipment development matures, leveraging these assets to enhance automated vehicle perception becomes increasingly valuable for more accurate and broader 3D object detection. This paper proposes a straightforward and scalable framework to incorporate infrastructure and vehicle onboard sensors to perform 3D object detection on Bird’s Eye View(BEV) images. And a cross-attention ba...Show More
Beat the Morning Rush: Survival Analysis-Informed DNNs With Collaborative Filtering to Predict Departure Times
Publication Year: 2025,Page(s):266 - 275
Congestion due to morning and evening traffic peaks has caused economic losses amounting to billions of dollars annually. Thus, accurately predicting the departure time of commuters is of interest to transportation planners, engineers, and elected officials alike. We develop a statistically-informed deep learning approach to improve commuter departure time prediction models. Specifically, we lever...Show More
IEEE Open Journal of Intelligent Transportation Systems Instructions for Authors
Publication Year: 2025,Page(s):C3 - C3
IEEE Open Journal of Intelligent Transportation Systems Instructions for Authors
Year: 2025 | Volume: 6
Contact Information
Editor-in-Chief
Associate Professor, Samueli School of Engineering; Director, UCLA Mobility Lab; Associate Director, UCLA Institute of Transportation Studies
University of California - Los Angeles
Los Angeles
USA
jiaqima@ucla.eduContact Information
Editor-in-Chief
Associate Professor, Samueli School of Engineering; Director, UCLA Mobility Lab; Associate Director, UCLA Institute of Transportation Studies
University of California - Los Angeles
Los Angeles
USA
jiaqima@ucla.eduIEEE Account
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