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6月30日周二
6月26日周五
6月24日周三
6月23日周二
  1. arXiv先进空中交通研究历史资料

    Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors

    The use of dedicated corridors for Advanced Air Mobility (AAM) traffic is one of the most commonly proposed pathways to integrating them into existing airspace operations. Most prior research has focused on the design of networks of AAM corridors and conflict resolution for aircraft within corridors. It is also generally believed that while attractive from an implementation perspective, corridor-based operations may be inefficient, especially in the absence of centralized traffic management. In this paper, we show that contrary to this belief, it is possible for autonomous aircraft to learn to self-organize into corridor flows in decentralized settings. We illustrate our approach using scenarios in which fixed-wing aircraft need to safely and efficiently traverse (1) a single corridor with metering after the exit, (2) a sequence of two consecutive corridors, and (3) a corridor that splits into two. We find that in decentralized settings with only local information, the aircraft are able to conform to the corridor boundaries more than 94% of the time and reach their goal in a relatively efficient manner. Furthermore, tactical interventions to handle violations of the separation minimum are needed only infrequently in low- and medium-density settings. However, such tactical interventions become more frequently necessary only when traffic density is high.（预印本；同行评审状态请核对原文。）

  2. arXiv先进空中交通研究历史资料

    Decentralized Autonomous Traffic Management through Corridor Networks

    As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numbers of crewed and uncrewed aircraft. Dedicated Advanced Air Mobility (AAM) corridors have therefore been proposed for organizing high-density autonomous traffic flows. The desire to scalably provide autonomous aircraft flexibility in trajectory planning motivates the development of decentralized approaches to traffic management in AAM corridors. In this work, we extend a multi-agent reinforcement learning (MARL) approach to address the challenge of decentralized traffic flow management in air corridor networks. We test policies trained in a single-corridor setting on increasingly complex multi-corridor networks with combinations of merges and splits in a zero-shot manner. Experimental results demonstrate that learned behaviors transfer well to scenarios with varying traffic density, network geometry, and heterogeneous vehicle performance, without needing centralized coordination or model retraining. We evaluate system-level performance in terms of conformance to corridor boundaries, completion rates, average speeds, distance traveled, and maintenance of inter-aircraft separation. We find that although our policies require only locally coordinated entry, traversal, and exit behaviors, they collectively produce desirable traffic flows through the corridor network.（预印本；同行评审状态请核对原文。）

6月22日周一
  1. arXiv先进空中交通研究历史资料

    Scalable Online Flight Trajectory Optimization via Sequential Quadratic Programming for Urban Air Mobility in Ultra Low-Altitude Airspace

    As Urban Air Mobility (UAM) scales toward high-density operations, generating collision-free trajectories within complex 3D cityscapes is a critical safety requirement. This paper proposes a scalable Sequential Quadratic Programming (SQP) framework that integrates geometric environmental constraints, operational limits, and vehicle dynamics within a single online trajectory optimization process. Rather than precomputing obstacle-free corridors ahead of time, our method encodes obstacle avoidance as live separating-hyperplane constraints regenerated at every solver iteration, so that dense urban geometry and full-DOF vehicle dynamics are resolved jointly and online as the reference and environment evolve. A variable-scale quadtree decomposition keeps computation bounded, enabling the framework to scale to city-wide environments while preserving real-time performance for high-speed flight. We validate the framework against conventional SQP, Iterative Linear Quadratic Regulator, and Differential Dynamic Programming across flights in five real-world urban centers, attaining 100% success and clearance rates on CPU-only hardware.（预印本；同行评审状态请核对原文。）

6月19日周五
  1. arXiv先进空中交通研究历史资料

    Distance-based subsidy rate design to incentivize ride-hail access to advanced air mobility hubs

    The success of advanced air mobility (AAM) operations is largely contingent on its effective integration with other ground transport modes. Under many use cases, AAM operators have to work with ride-hailing operators to create a seamless air taxi travel experience with adequate first and last-mile access. In investigating this multimodal coalition, this study proposes a distance-based subsidy rate design for AAM operators to incentivize ride-hail access to AAM hubs, incorporating air mobility operators' profitability considerations and travelers' route choices jointly. Using New York City (NYC) airport access as a case study, this study integrates high-volume for-hire vehicle (HVFHV) data from NYC taxi zones to consider real-world spatial demand distributions while considering passenger groups with different values of time (VOT) to derive insights on distinctive customer bases. Overall, the results show that AAM operators would need to subsidize the ride-hailing operators on vertiport access trips when air taxi operating costs exceed $12/mi. The analysis of ridership at AAM hubs indicates that ridership and profit contributions differ across different candidate vertiports in Manhattan, reflecting spatial demand heterogeneity. Additionally, having the airport access system in place, the taxi zones that generate the highest passenger demand to all three major NYC airports are identified under lower air taxi fare scenarios. These findings highlight how a distance-based subsidy rate design is beneficial in facilitating better access to vertiports and to foster high air taxi rid（预印本；同行评审状态请核对原文。）

6月18日周四
  1. arXiv先进空中交通研究历史资料

    From Sentiment to Actionable Insights: Public Sentiment Analysis of Advanced Air Mobility

    Advanced Air Mobility (AAM) is an emerging low-altitude transportation system whose successful deployment depends on both technological progress and public acceptance. Public acceptance can influence government support, regulations, noise standards, willingness to fly, and the commercial viability of AAM. Understanding public sentiment is therefore essential for identifying societal barriers and developing effective adoption strategies. This study analyzes 306,009 human-generated texts collected from Reddit and Quora to examine AAM-related public discourse using artificial intelligence models. Seven sentiment-analysis approaches, including lexicon-based, machine-learning, deep-learning, and transformer models, are evaluated to identify the most reliable method for AAM-specific sentiment classification. ModernBERT achieves the highest performance and is used to label the full dataset. Latent Dirichlet Allocation is then applied within each sentiment class to identify underlying topics and examine their temporal evolution from 2008 to 2025. The analysis identifies 20 topics and six major cross-sentiment clusters: workforce and skill development, regulation and compliance, drone technical performance, military and geopolitical applications, safety and operational risks, and noise and disturbance. These findings can help policymakers, industry stakeholders, researchers, and operators develop targeted regulations, safety measures, workforce programs, noise-reduction strategies, and public communication efforts to address concerns and support the responsible deployment of AAM.（预印本；同行评审状态请核对原文。）

6月17日周三
6月15日周一
6月13日周六
  1. arXiv先进空中交通研究历史资料

    An Analytical Methodology for Quantifying Airspace Conflict Rate and Complexity

    Air traffic growth, advanced air mobility, and increasingly autonomous operations are driving the need for scalable and adaptive airspace design methodologies. Central to this challenge is quantifying how traffic flow structure and demand, governed in part by airspace geometry, influence conflict generation and operational complexity. This paper presents an analytical framework for computing conflict rate and conflict probability in structured airspace using stochastic flow models. Traffic streams are modeled as renewal processes with prescribed inter-arrival time distributions, while interactions between flows are captured through geometry-dependent minimum spacing constraints at merges and crossings. Within this formulation, closed-form upper bounds on the expected conflict rate and conflict probability per aircraft are derived as functions of flow configuration and demand. These metrics are interpreted as complementary measures of airspace complexity, reflecting controller workload and per-aircraft operational risk. The methodology is applied to representative hexagonal cell geometries with varying routing structures and flow distributions. Results reveal non-monotonic tradeoffs between routing flexibility, capacity, and conflict generation, with intermediate flow configurations outperforming both highly constrained and highly distributed cases. The proposed framework provides a tractable tool for evaluating airspace design alternatives and complexity-informed traffic management strategies.（预印本；同行评审状态请核对原文。）

6月12日周五
  1. 溯驭技术氢动力与航空电控新闻历史资料

    FCVC 2026首日直击｜溯驭技术多款氢能电控智能体产品亮相，全新无人机开发平台备受行业瞩目

    6月10日，第十届国际氢能与燃料电池汽车大会暨展览会（FCVC 2026）在江苏昆山花桥国际博览中心盛大开幕，溯驭技术携多款智能化电控产品重磅亮相。作为面向未来氢能源的一体化电控领域领军企业，溯驭技术本次推出的电控智能体产品和系列创新开发平台方案全面覆盖低空应用、分布式供能、绿色出行等多元化场景，首日即成为展会焦点。展会首日，中国科学技术协会主席万钢亲临溯驭（披露日：2026-06-12）

6月11日周四
6月10日周三
6月8日周一
  3. arXiv先进空中交通研究历史资料

    Adaptive Model Predictive Control of Nonlinear Generic Urban Air Mobility Using Linear Parameter-Varying Systems

    This paper presents an adaptive model predictive control (MPC) framework for nonlinear urban air mobility (UAM) vehicles operating across the full flight envelope. The proposed approach leverages a linear parameter-varying (LPV) representation to update the predictive model online, enabling accurate capture of strongly nonlinear and time-varying dynamics associated with distributed electric propulsion (DEP) eVTOL aircraft. To systematically address the high-dimensional and coupled nature of MPC tuning, a multi-objective evolutionary optimization strategy based on NSGA-II is employed, incorporating proper normalization of states and control inputs to ensure balanced weighting and meaningful exploration of the design space. The resulting controller explicitly accounts for actuator constraints and enables reconfigurable control allocation for fault-tolerant operation. The framework is evaluated in nonlinear simulations using NASA's Generic Urban Air Mobility (GUAM) model and benchmarked against a robust servomechanism linear quadratic regulator (RSLQR). Results demonstrate that the proposed adaptive MPC achieves improved trajectory tracking and enhanced robustness under both nominal conditions and actuator degradation scenarios, including partial motor failure, while maintaining constraint satisfaction throughout all flight regimes.（预印本；同行评审状态请核对原文。）

6月7日周日
6月5日周五
6月4日周四