Traffic Management Research Institute

Collaborative coverage path planning for multiple unmanned aerial vehicles (UAVs) in complex regions presents significant challenges in efficiency and robustness. This paper proposes an integrated path planning method that fuses a two-level grid model, a turn-aware weighted Minimum Spanning Tree (MST) skeleton, and Spanning Tree Covering (STC) expansion. At a coarse resolution, a turn-aware weighted MST skeleton is constructed to ensure global connectivity and reduce sharp turns. At a fine resolution, a single, non-self-intersecting closed loop is generated via STC expansion to guarantee coverage completeness. Furthermore, this method achieves balanced multi-UAV task allocation through an equal arc-length partitioning scheme and a novel free-endpoint strategy, with time-window scheduling to prevent flight conflicts. Simulations conducted in various environments demonstrate that the proposed method achieves 100% coverage. Compared to baseline algorithms in complex scenarios, it significantly reduces the total path length by up to 20.1% and the maximum completion time by up to 30.5%. The results affirm that this framework provides an efficient, scalable, and robust solution for multi-UAV coverage operations in applications such as forest inspection.

Airspace traffic complexity is closely related to the safety and operational efficiency of civil aviation. To enhance the accuracy of complexity assessment, this paper proposes an adaptive metric learning based airspace traffic complexity assessment method(ATCA-AML). First, a multi-resolution air traffic image dataset is constructed to capture the variation of airspace operational states across different spatiotemporal scales. On this basis, an adaptive metric learning model is designed, which takes multi-resolution images as input and employs a dynamic multi-proxy generation method to optimize the distribution of samples in the embedding space. This enables effective classification of airspace traffic complexity into multiple levels. Experiments conducted on real data from the Central-Southern China airspace demonstrate that the spatiotemporal resolution of traffic images has a significant impact on assessment performance. Moreover, compared to existing approaches, the proposed method achieves superior accuracy and discriminative capability, offering a more precise reflection of the dynamic complexity of airspace operations.

Vertiports, as the foundational ground infrastructure for Urban Air Mobility (UAM), have garnered increasing scholarly attention in recent years. To examine how the existing literature has reviewed and summarized vertiport-related knowledge, this study conducts a bibliometric analysis of publications (2000–2024) from four major databases, including Web of Science and Scopus, using VOSviewer and CiteSpace. By analyzing co-citation and keyword co-occurrence patterns, the results suggest that vertiport research frontiers are shifting toward facility location, network planning, airspace and scheduling management, scalable infrastructure, and integration with ground transport systems. Scholars and institutions in the United States, China, Europe, and South Korea have taken leading roles in advancing this field, though collaboration among research organizations still requires strengthening. Overall, the findings reveal future research pathways and provide support for the planning and integration of vertiport infrastructure in UAM operations.

Data dashboards can visually present data for filtering, interaction and data analysis in a dynamic and intuitive way. In the automotive sector, test engineers are interested in monitoring and analyzing vehicle measurement data from test drives. Data sections recorded under specific conditions are especially relevant, showing important scenarios of the driving operation. This work explores Large Language Models (LLM) assisting in data filtering requests that could help the user identify relevant dashboard filter settings for their domain-specific questions. We use function calling to select functions and corresponding parameter values from a provided set of analysis functions. We tested our method using GPT-3.5- Turbo and zero-shot generalization with a generated test data set of user prompts. The results show that correct functions and parameters can be selected by the model, but iterative and use case-specific adaptations and fine-tuning might be required to achieve a reliable automatic functionality in a dashboard application. We propose possible further approaches, with user studies being relevant for the exploration of productive usage.

With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology.

Abstract Camera-LiDAR fusion has shown great promise in advancing road detection for autonomous driving, combining the semantic richness of RGB imagery with the depth accuracy of LiDAR. However, its practical deployment faces two main challenges: (1) the high cost and limited accessibility of LiDAR sensors hinder their large-scale adoption, and (2) the scarcity of labeled data in unseen target domains limits the generalization capability of supervised methods. To overcome these limitations, we propose SPADA-Road, an unsupervised framework that integrates Superpixel-guided segmentation with a novel Pseudo-LiDAR (PL) generation module. Our PL module synthesizes depth cues from monocular RGB inputs, effectively augmenting training data without requiring physical LiDAR sensors. To enable robust cross-domain generalization, we adopt an adversarial domain adaptation strategy that aligns feature distributions between labeled source and unlabeled target domains. We evaluate SPADA-Road on the KITTI Road dataset for training, and validate its performance on two LiDAR-free benchmarks—Cityscapes and CamVid. Extensive experiments demonstrate that our method achieves superior performance compared to several state-of-the-art baselines, highlighting its effectiveness in LiDAR-free and label-scarce scenarios.

Micro-nano additive-enhanced lubricating greases are pivotal for extreme-condition tribology, yet optimizing synergistic additive concentrations remains constrained by conventional experimental designs. This study employs a central composite design (CCD) coupled with MATLAB response surface methodology to precisely determine optimal concentrations of nano-graphite (N-G), graphene (GN), and potassium borate (PB) in titanium complex grease. Fifteen formulations were tested under progressive loads (98-598 N) via four-ball tribometry, with SEM/XPS characterizing wear mechanisms. The synergistic grease (G-MX: 0.83 wt% N-G, 0.05 wt% GN, 2.59 wt% PB) reduced the average friction coefficient by 45.3% and wear scar diameter by 23.3% versus base grease, surpassing single-additive variants. The CCD-MATLAB framework addressed sampling limitations of prior orthogonal methods, enabling optimization beyond discrete testing points. Mechanistic analysis revealed a dual lubrication regime: physically adsorbed films (soap molecules and refined PB particles) dominated at low loads, while chemically bonded tribofilms (Fe₃C, B₂O₃, TiO₂) ensured wear resistance under extreme pressures.

In many real-life applications, wireless chargers are deployed outdoor or in public area or even unattended environment such as hotels, restaurants, retail stores. They are exposed to various risks and malicious attacks that may break them down and further incur significant cost (e.g., battery replacement and maintenance) or performance degradation. Hence, we consider the problem of <u>F</u>ault-tolerant w<u>I</u>reless cha<u>R</u>ger place<u>M</u>ent (FIRM): given a set of wireless chargers and a set of tasks to be collaboratively conducted by a set of rechargeable devices, determining where to deploy the chargers to maximize the worst-cast overall task charging utility subject to the constraint that up to <inline-formula><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> chargers may break down. FIRM is a non-linear combinatorial two-level optimization problem. We first consider a relaxed version of FIRM (FIRM-R for short) corresponding to the inner optimization problem in FIRM. To address FIRM-R, we first propose an area discretization scheme to convert the infinite solution space into finite candidate positions. We then devise a power allocation method, based on which we prove that FIRM-R falls into the realm of maximizing a monotone submodular function under a uniform constraint. We then propose a constant-factor approximation algorithm to solve FIRM-R. Taking the above approximation algorithm as a subroutine, we further develop an approximation algorithm that solves FIRM with a constant-factor approximation ratio. Our extensive simulations and field experiments demonstrate that the overall charging utility of our proposed algorithm FIRM considering fault tolerance by greedy removal of <inline-formula><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> chargers outperforms the that of FIRM-R without considering fault tolerance by greedy removal of <inline-formula><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> chargers by at least 119.89%.