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NASA NTRS2002

Use of Traffic Intent Information by Autonomous Aircraft in Constrained Operations

This paper presents findings of a research study designed to provide insight into the issue of intent information exchange in constrained en-route air-traffic operations and its effect on pilot decision-making and flight performance. The piloted simulation was conducted in the Air Traffic Operations Laboratory at the NASA Langley Research Center. Two operational modes for autonomous flight management were compared under conditions of low and high operational complexity (traffic and airspace hazard density). The tactical mode was characterized primarily by the use of traffic state data for conflict detection and resolution and a manual approach to meeting operational constraints. The strategic mode involved the combined use of traffic state and intent information, provided the pilot an additional level of alerting, and allowed an automated approach to meeting operational constraints. Operational constraints applied in the experiment included separation assurance, schedule adherence, airspace hazard avoidance, flight efficiency, and passenger comfort. The strategic operational mode was found to be effective in reducing unnecessary maneuvering in conflict situations where the intruder's intended maneuvers would resolve the conflict. Conditions of high operational complexity and vertical maneuvering resulted in increased proliferation of conflicts, but both operational modes exhibited characteristics of stability based on observed conflict proliferation rates of less than 30 percent. Scenario case studies illustrated the need for maneuver flight restrictions to prevent the creation of new conflicts through maneuvering and the need for an improved user interface design that appropriately focuses the pilot's attention on conflict prevention information. Pilot real-time assessment of maximum workload indicated minimal sensitivity to operational complexity, providing further evidence that pilot workload is not the limiting factor for feasibility of an en-route distributed traffic management system, even under highly constrained conditions.

Wing, David J.↗

DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

SIRIUS: Simulation Infrastructure for Research on Interoperating Unmanned Systems

This paper presents the Simulation Infrastructure for Research on Interoperating Unmanned Systems (SIRIUS), a research framework for simulation and analysis of future conceptual Urban Air Mobility (UAM) operations. SIRIUS is being developed under the auspices of the NASA Air Traffic Management eXploration project, UAM subproject (ATM-X UAM). SIRIUS provides an intuitive, highly configurable graphical user interface to design complex traffic scenarios and airspace configurations representative of conceptual UAM operations. Aircraft simulated with SIRIUS can be equipped with flight-tested capabilities for detect and avoid (DAA), geofencing, distributed merging and spacing, path conformance, and path planning while executing time-constrained, 4D trajectories generated by a UAM ground operations system. Central to the design of the SIRIUS simulation framework is the capability to evaluate the integration and interoperability of ground-based separation services (e.g., strategic separation) with extended DAA functionality (e.g., path monitoring, separation provision, merging and spacing, etc.) The simulation environment also supports modelling of wind, navigation, and sensor uncertainties, as well as communication delays. SIRIUS enables distributed simulation of large-scale scenarios. An interactive graphical analysis capability helps isolate, visualize, and compare relevant vehicle state data and widely used measures of performance metrics across multiple scenarios.

Andrew Peters↗

NASA NTRS2010

Automation for Accommodating Fuel-Efficient Descents in Constrained Airspace

Continuous descents at low engine power are desired to reduce fuel consumption, emissions and noise during arrival operations. The challenge is to allow airplanes to fly these types of efficient descents without interruption during busy traffic conditions. During busy conditions today, airplanes are commonly forced to fly inefficient, step-down descents as airtraffic controllers work to ensure separation and maximize throughput. NASA in collaboration with government and industry partners is developing new automation to help controllers accommodate continuous descents in the presence of complex traffic and airspace constraints. This automation relies on accurate trajectory predictions to compute strategic maneuver advisories. The talk will describe the concept behind this new automation and provide an overview of the simulations and flight testing used to develop and refine its underlying technology.

Coopenbarger, Richard A.↗

DOE OSTI2026

IPC-Fusion (Infrastructure Perception and Control (IPC): Multisensor Data Fusion Software) [SWR-25-153]

As part of the National Laboratory of the Rockies' (NLR’s) Infrastructure Perception and Control Laboratory, the IPC-Fusion toolkit provides a probabilistic, scalable, multi-sensor fusion framework that integrates (late-stage fusion) heterogeneous object detection data from traffic sensors to enable robust, real-time tracking of roadway occupants. The algorithmic design of the toolkit is motivated by the need for creating a digital twin of traffic at the edge in a scalable and affordable manner. The software operates by combining object-level measurements (such as position and velocity) from a suite of sensors (such as radar, lidar, camera) using Kalman filtering and probabilistic data association techniques to overcome individual sensor limitations and achieve superior tracking performance in complex traffic zones. The framework addresses key challenges including heterogeneous measurement uncertainties, asynchronous data streams, varying spatiotemporal data resolutions, robust data association, and adaptive object lifecycle management. Validated on real-world traffic intersection data including vehicles and pedestrians, IPC-Fusion demonstrates enhanced tracking reliability across scenarios involving occlusions, sensor failures, and varying traffic densities, supporting the broader IPC initiative's goal of transforming transportation infrastructure through advanced perception capabilities for intelligent transportation systems, traffic safety applications, and autonomous vehicle support.

Sandhu, Rimple [National Laboratory of the Rockies↗

NASA NTRS2011

Evaluation of High Density Air Traffic Operations with Automation for Separation Assurance, Weather Avoidance and Schedule Conformance

In this paper we discuss the development and evaluation of our prototype technologies and procedures for far-term air traffic control operations with automation for separation assurance, weather avoidance and schedule conformance. Controller-in-the-loop simulations in the Airspace Operations Laboratory at the NASA Ames Research Center in 2010 have shown very promising results. We found the operations to provide high airspace throughput, excellent efficiency and schedule conformance. The simulation also highlighted areas for improvements: Short-term conflict situations sometimes resulted in separation violations, particularly for transitioning aircraft in complex traffic flows. The combination of heavy metering and growing weather resulted in an increased number of aircraft penetrating convective weather cells. To address these shortcomings technologies and procedures have been improved and the operations are being re-evaluated with the same scenarios. In this paper we will first describe the concept and technologies for automating separation assurance, weather avoidance, and schedule conformance. Second, the results from the 2010 simulation will be reviewed. We report human-systems integration aspects, safety and efficiency results as well as airspace throughput, workload, and operational acceptability. Next, improvements will be discussed that were made to address identified shortcomings. We conclude that, with further refinements, air traffic control operations with ground-based automated separation assurance can routinely provide currently unachievable levels of traffic throughput in the en route airspace.

Prevot, Thomas↗

NASA NTRS2010

Complexity Analysis of Traffic in Corridors-in-the-Sky

The corridors-in-the-sky concept imitates the highway system in ground transportation. The benefit expected from a corridor relies on its capability of handling high density traffic with negligible controller workload, the acceptance of extra fuel or distance, and the complexity reduction in underlying sectors. This work evaluates a selected corridor from these perspectives through simulations. To examine traffic inside the corridor, a corridor traffic simulation tool that can resolve conflicts is developed using C language. Prescribed conflict resolution maneuvers mimic corridor users behaviors and conflict resolution counts measure complexity. Different lane options and operational policies are proposed to examine their impacts on complexity. Fuel consumption is calculated and compared for corridor traffic. On the other hand, to investigate the complexity of non-corridor traffic in underlying sectors, the existing Airspace Concept Evaluation System tool is utilized along with the Automated Airspace Concept tool. The number of conflict resolutions is examined and treated as the complexity measurement. The results show heavy traffic can be managed with low complexity for a historical traffic schedule simulated with appropriate operational policies and lane options. For instance, with 608 flights and peak aircraft count of 100, only 84 actions need to be taken in a 24-hour period to resolve the conflicts for an 8-lane corridor. Compared with the fuel consumptions with great circle trajectories, the simulation of corridor traffic shows that the total extra fuel for corridor flights is 26,373 gallons, or 2.76%, which is 0.38% less than flying filed flight plans. Without taking climb and descent portions of corridor traffic, the complexity of underlying sectors is reduced by 17.71%. However the climb and descent portions will eliminate the reduction and the overall complexity of sectors is actually increased by 9.14%.

Xue, Min↗

NASA NTRS2011

GIS Data Based Automatic High-Fidelity 3D Road Network Modeling

3D road models are widely used in many computer applications such as racing games and driving simulations_ However, almost all high-fidelity 3D road models were generated manually by professional artists at the expense of intensive labor. There are very few existing methods for automatically generating 3D high-fidelity road networks, especially those existing in the real world. This paper presents a novel approach thai can automatically produce 3D high-fidelity road network models from real 2D road GIS data that mainly contain road. centerline in formation. The proposed method first builds parametric representations of the road centerlines through segmentation and fitting . A basic set of civil engineering rules (e.g., cross slope, superelevation, grade) for road design are then selected in order to generate realistic road surfaces in compliance with these rules. While the proposed method applies to any types of roads, this paper mainly addresses automatic generation of complex traffic interchanges and intersections which are the most sophisticated elements in the road networks

Wang, Jie↗

NASA NTRS2019

Increasing Pilot’s Understanding of Future Automation State – an Evaluation of an Automation State and Trajectory Prediction System

A pilot in the loop flight simulation study was conducted at NASA Langley Research Center to evaluate a trajectory prediction system. The trajectory prediction system computes a five-minute prediction of the lateral and vertical path of the aircraft given the current and intent state of the automation. The prediction is shown as a graphical representation so the pilots can form an accurate mental model of the future state. Otherwise, many automation changes and triggers are hidden from the flight crew or need to be consolidated to understand if a change will occur and the exact timing of the change. Varying dynamic conditions like deceleration can obscure the future trajectory and the ability to meet constraints, especially in the vertical dimension. Current flight deck indications of flight path assume constant conditions and do not adequately support the flight crew to make correct judgments regarding constraints. The study was conducted using ten commercial airline crews from multiple airlines, paired by airline to minimize procedural effects. Scenarios spanned a range of conditions that provided evaluation in a realistic environment with complex traffic and weather conditions. In particular, scenarios probed automation state and loss of state awareness. The technology was evaluated and contrasted with current state-of-the-art flight deck capabilities modeled from the Boeing 787. Objective and subjective data were collected from aircraft parameters, questionnaires, audio/video recordings, head/eye tracking data, and observations. This paper details findings about the trajectory prediction system including recommendations about further study.

Etherington, Timothy J.↗

NASA NTRSDate not supplied

Rolling Horizon with K-Position Search Method for Strategic Deconfliction of Package Delivery UAS

This research focuses on the strategic deconfliction of unmanned aircraft systems (UAS) in an urban package delivery environment with two depots and multiple drop-off locations. Since the formulated mixed-integer nonlinear programming (MINLP) problem is non-deterministic polynomial-time (NP) hard, a heuristic algorithm called "rolling horizon with k-position search (KPS)" is used to compute the departure sequence and scheduled time of departure (STD) of each UAS at a depot, considering temporal constraints at en-route crossing waypoints and depots for strategic deconfliction. The simulation studies show that an increase in the value of k (local neighborhood search) in the KPS reduces the average ground delay at the cost of an increase in the computation time for a given number of UAS, size of the rolling horizon window, and number of depots involved in the local neighborhood search. The studies also show that for a given rolling horizon window, the computation time increases exponentially with an increase in the total number of UAS flights when serial processing the local neighborhood search of KPS (with k > 1) and drops by an order of magnitude upon performing the local neighborhood search of KPS using parallel processing instead of serial processing. The computation time drops with the reduction in air traffic complexity of a scenario for a given number of flights, k (local neighborhood search), and rolling horizon window.

UTM↗

NASA NTRS2013

Control of Future Air Traffic Systems via Complexity Bound Management

The complexity of the present system for managing air traffic has led to "discreteness" in approaches to creating new concepts: new concepts are created as point designs, based on experience, expertise, and creativity of the proposer. Discrete point designs may be highly successful but they are difficult to substantiate in the face of equally strong substantiation of competing concepts, as well as the state of the art in concept evaluation via simulations. Hybrid concepts may present a compromise - the golden middle. Yet a hybrid of sometimes in principle incompatible concepts forms another point design that faces the challenge of substantiation and validation. We are faced with the need to re-design the air transportation system ab initio. This is a daunting task, especially considering the problem of transitioning from the present system to any fundamentally new system. However, design from scratch is also an opportunity to reconsider approaches to new concept development. In this position paper we propose an approach, Optimized Parametric Functional Design, for systematic development of concepts for management and control of airspace systems, based on optimization formulations in terms of required system functions and states. This reasoning framework, realizable in the context of ab initio system design, offers an approach to deriving substantiated airspace management and control concepts. With growing computational power, we hope that the approach will also yield a methodology for actual dynamic control of airspace

Alexandrov, Natalia↗

NASA NTRS2019

Scenario Complexity for Unmanned Aircraft System Traffic

This work introduces an approach to estimate the complexity of a low-altitude air traffic scenario involving multiple UASs using mathematical programming. Given a set of multi-point UAS flight trajectories, vehicle dynamics, and a conflict resolution algorithm, an abstract model is developed such that it can be solved quickly using a mathematical programming optimization software without running high-fidelity simulations that can be computationally expensive and may not suit real-time apA quick and accurate assessment of complexity for a given traffic scenario can help plan and schedule flights to alleviate traffic bottleneck and mitigate operation risks, especially for unmanned aerial system traffic management where high traffic density or complexity is expected. This work introduces a traffic scenario complexity metric that was constructed based on the number of potential conflicts weighted by the conflict resolution cost associated. The cost associated with a conflict is calculated based on the corresponding conflict resolution maneuvers. To obtain the conflict resolution maneuvers, a MILP-based optimization was formulated with the vehicle model and conflict management parameters incorporated. To evaluate the complexity metrics, an approach of using measurements from high-fidelity simulations was proposed. The scenario complexity measurements for 920 random-generated scenarios were obtained through high-fidelity simulations and treated as the ground truth. Two statistics methods: Pearson and Alternative Conditional Expectations were applied for analysis. The results showed that the number of flights has low correlation with the scenario complexity according to the correlation coefficients calculated by both methods. The Alternative Conditional Expectations method shows that the proposed scenario complexity metric has better correlation with the ground truth than the number of potential conflicts.plications. In the abstract model, each vehicle is represented by a time-varied vector associated with position, speed, and heading information. The total extra distance that aircraft need to divert from their original routes to avoid collisions is computed and used to setup a quadratic programming formula. The metrics including the number of conflicts and extra distances travelled by all vehicles are then utilized to estimate the complexity of a given UAS flight scenario. Results and verification against high-fidelity simulations will be provided in the final draft.

traffic complexity↗

DOE OSTI2023

Emissions minimization on road networks via Generic Second Order Models

In this paper we consider the problem of estimating emissions due to vehicular traffic on complex networks, and minimizing their effect by regulating traffic at junctions. For the traffic evolution, we consider a Generic Second Order Model, which encompasses the majority of two-equations (i.e., second-order) models available in the literature, and extend it to road networks with merge and diverge junctions. The dynamics on the whole network is determined by selecting a solution to the Riemann Problems at junctions, i.e., the Cauchy problems with constant initial data on each incident road. The latter are solved by assuming the maximization of the flow and assigning a traffic distribution coefficient for outgoing roads of diverges, and a priority rule for incoming roads of merges. A general emission model is considered and its parameters are tuned to the $ {\mathrm{NO_{x}}} $ emission rate. The minimization of emissions is then formulated in terms of the traffic distribution and priority parameters, taking into account travel times. A comparison is provided between roundabouts with optimized parameters and traffic lights, which correspond to time-varying traffic priorities. Our approach can be adapted to manage traffic in complex networks in order to reduce emissions while keeping travel time at acceptable levels.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2019

Scenario Complexity for Unmanned Aircraft System Traffic

UTM↗

NASA NTRS2019

Complex Dynamics of Air Traffic Flow

Air traffic in the United States has continued to grow at a steady pace since 1980, except for a dip immediately after the tragic events of September 11, 2001. There are different growth scenarios associated both with the magnitude and the composition of the future air traffic. The Terminal Area Forecast (TAF), prepared every year by the FAA, projects the growth of traffic in the United States. Both Boeing and Airbus publish market outlooks for air travel annually. Although predicting the future growth of traffic is difficult, there are two significant trends: heavily congested major airports continue to see an increase in traffic, and the emergence of regional jets and other smaller aircraft with fewer passengers operating directly between non-major airports. The interaction between air traffic demand and the ability of the system to provide the necessary airport and airspace resources can be modeled as a network. The size of the resulting network varies depending on the choice of its nodes. It would be useful to understand the properties of this network to guide future design and development. Many questions, such as the growth of delay with increasing traffic demand and impact of the en route weather on future air traffic, require a systematic understanding of the properties of the air traffic network. There has been a major advance in the understanding of the behavior of networks with a large number of components. Several theories have been advanced about the evolution of large biological and engineering networks by authors in diversified disciplines like physics, mathematics, biology and computer science. Several networks exhibit a scale-free property in the sense that the probabilistic distribution of their nodes as a function of connections decreases slower than an exponential. These networks are characterized by the fact that a small number of components have a disproportionate influence on the performance of the network. Scale-free networks are tolerant to random failure of components, but are vulnerable to selective attack on components. This paper examines two network representations for the baseline air traffic system. A network defined with the 40 major airports as nodes and with standard flight routes as links has a characteristic scale: all nodes have 60 or more links and no node has more than 460 links. Another network is defined with baseline aircraft routing structure exhibits an exponentially truncated scale-free behavior. Its degree ranges from 2 connections to 2900 connections, and 225 nodes have more than 250 connections. Furthermore, those high-degree nodes are homogeneously distributed in the airspace. A consequence of this scale-free behavior is that the random loss of a single node has little impact, but the loss of multiple high-degree nodes (such as occurs during major storms in busy airspace) can adversely impact the system. Two future scenarios of air traffic growth are used to predict the growth of air traffic in the United States. It is shown that a three-times growth in the overall traffic may result in a ten-times impact on the density of traffic in certain parts of the United States.

Scale-free Networks↗

NASA NTRS2011

Complexity Management Using Metrics for Trajectory Flexibility Preservation and Constraint Minimization

The growing demand for air travel is increasing the need for mitigating air traffic congestion and complexity problems, which are already at high levels. At the same time new surveillance, navigation, and communication technologies are enabling major transformations in the air traffic management system, including net-based information sharing and collaboration, performance-based access to airspace resources, and trajectory-based rather than clearance-based operations. The new system will feature different schemes for allocating tasks and responsibilities between the ground and airborne agents and between the human and automation, with potential capacity and cost benefits. Therefore, complexity management requires new metrics and methods that can support these new schemes. This paper presents metrics and methods for preserving trajectory flexibility that have been proposed to support a trajectory-based approach for complexity management by airborne or ground-based systems. It presents extensions to these metrics as well as to the initial research conducted to investigate the hypothesis that using these metrics to guide user and service provider actions will naturally mitigate traffic complexity. The analysis showed promising results in that: (1) Trajectory flexibility preservation mitigated traffic complexity as indicated by inducing self-organization in the traffic patterns and lowering traffic complexity indicators such as dynamic density and traffic entropy. (2)Trajectory flexibility preservation reduced the potential for secondary conflicts in separation assurance. (3) Trajectory flexibility metrics showed potential application to support user and service provider negotiations for minimizing the constraints imposed on trajectories without jeopardizing their objectives.

Idris, Husni↗

NASA NTRSDate not supplied

An Impedance-Based Complexity Metric for Unmanned Aircraft System Traffic Scenario Classification

This paper introduces an impedance-based metric to capture the complexity of a given unmanned aircraft system traffic scenario. The metric accounts for both the number of aircraft and the traffic flow pattern. The work presented here extends an earlier approach that introduced another scenario complexity metric based on the number of potential conflicts weighted by the conflict resolution cost associated. Complexity measurements for randomly-generated scenarios were produced through high-fidelity fast-time simulations and treated as baseline. Then the impedance based metric was evaluated, for the same scenarios, without the need for an actual flight simulation and a conflict resolution method. The results show that the impedance-based metric has a strong correlation to the baseline data and performs marginally better than the weighted conflict-based complexity metric introduced in the earlier work. The metric computation generates impedance maps which are useful for identifying high complexity regions in a scenario, where flight plan changes might be necessitated. This metric can therefore be used, in conjunction with other complexity metrics, to inform adequate traffic management strategies and classify a traffic scenario as acceptable, unacceptable or acceptable with changes made to flight plans that pass through the high complexity regions. The metric can also be used as a guidance metric for strategic conflict management methods.

Complexity↗

NASA NTRS2002

A Flight Deck Decision Support Tool for Autonomous Airborne Operations

NASA is developing a flight deck decision support tool to support research into autonomous operations in a future distributed air/ground traffic management environment. This interactive real-time decision aid, referred to as the Autonomous Operations Planner (AOP), will enable the flight crew to plan autonomously in the presence of dense traffic and complex flight management constraints. In assisting the flight crew, the AOP accounts for traffic flow management and airspace constraints, schedule requirements, weather hazards, aircraft operational limits, and crew or airline flight-planning goals. This paper describes the AOP and presents an overview of functional and implementation design considerations required for its development. Required AOP functionality is described, its application in autonomous operations research is discussed, and a prototype software architecture for the AOP is presented.

Ballin, Mark G.↗