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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3

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DOE OSTI2026

Detect the Unobservable: Abnormality Detection in mixed Autonomy for Lane Change Maneuver with Following Vehicles’ Trajectories Only

Highly Automated Vehicles (HAVs) and Advanced Driver-Assistance Systems (ADAS) are transforming modern transportation with enhanced mobility, safety, and efficiency. Despite their advantages, cybersecurity vulnerabilities in these systems can lead to abnormal behavior, posing significant risks to surrounding human-driven vehicles (HDVs) in mixed traffic environments. Here, this article addresses the challenge of detecting abnormal lateral movements of HAVs/ADAS vehicles using only trajectory profiles of following HDVs. Specifically, we propose a novel modeling approach that captures both normal and abnormal lateral behaviors through vehicle kinematics, integrated decision-making processes, vehicle control using symbolic regression for lane change vehicles. Additionally, we introduce an abnormality detection framework that relies on observable HDV data, even in occlusion scenarios. The framework evaluates the sensitivity of various car-following models to detect abnormal behaviors, providing insights into the interaction between HAVs/ADAS and HDVs in mixed autonomy systems.

Connected and Automated vehicles↗

DOE OSTI2022

Optimizing lane reversals in transportation networks to reduce traffic congestion: A global optimization approach

This paper studies how to reduce the overall travel time of commuters in a transportation network by reversing the direction of some lanes in the network using a macroscopic network-wide perspective. Similar to the Network Design Problem, the lane reversal problem has been shown to be NP-hard given the dependence of the users’ route selection on the lane direction decision. Herein, we propose and compare three efficient methods to solve the routing and lane reversal problem jointly. First, we introduce an alternating method that decouples the routing and lane assignment problems. Second, we propose a Frank–Wolfe method that jointly takes gradient steps to adjust both the lane assignment and routing decisions. Third, we propose a convex approximation method that uses a threshold-based approach to convexify the joint routing and lane reversal objective. The convex approximation method is advantageous since it finds a global optimum solution for the approximated problem and it enables the possibility to include linear constraints. Using this method, we extend the main formulation to be able to limit a maximum number of reversed lanes, as well as to incorporate multiple origin–destination (OD) patterns. We test the proposed methods in a case study using the transportation network of Eastern Massachusetts where our results indicate an overall reduction in travel times of 4.7% by selecting the best 15 reversals. Moreover, using a small test network, we investigate the performance of the lane reversal strategies as a function of the OD demand symmetry. As expected, we observe that when the OD demand is very asymmetric (e.g., for a single OD pair, evacuations, large events), the reduction in travel times is larger than the symmetric case, reaching travel time reductions of 60%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS1991

Aviation safety and automation technology for subsonic transports

Discussed here are aviation safety human factors and air traffic control (ATC) automation research conducted at the NASA Ames Research Center. Research results are given in the areas of flight deck and ATC automations, displays and warning systems, crew coordination, and crew fatigue and jet lag. Accident investigation and an incident reporting system that is used to guide the human factors research is discussed. A design philosophy for human-centered automation is given, along with an evaluation of automation on advanced technology transports. Intelligent error tolerant systems such as electronic checklists are discussed along with design guidelines for reducing procedure errors. The data on evaluation of Crew Resource Management (CRM) training indicates highly significant positive changes in appropriate flight deck behavior and more effective use of available resources for crew members receiving the training.

Albers, James A.↗

NASA NTRS1994

Ground vehicle control at NIST: From teleoperation to autonomy

NIST is applying their Real-time Control System (RCS) methodology for control of ground vehicles for both the U.S. Army Researh Lab, as part of the DOD's Unmanned Ground Vehicles program, and for the Department of Transportation's Intelligent Vehicle/Highway Systems (IVHS) program. The actuated vehicle, a military HMMWV, has motors for steering, brake, throttle, etc. and sensors for the dashboard gauges. For military operations, the vehicle has two modes of operation: a teleoperation mode--where an operator remotely controls the vehicle over an RF communications network; and a semi-autonomous mode called retro-traverse--where the control system uses an inertial navigation system to steer the vehicle along a prerecorded path. For the IVHS work, intelligent vision processing elements replace the human teleoperator to achieve autonomous, visually guided road following.

Murphy, Karl N.↗

NASA NTRS1987

A conceptual framework for intelligent real-time information processing

By combining artificial intelligence concepts with the human information processing model of Rasmussen, a conceptual framework was developed for real time artificial intelligence systems which provides a foundation for system organization, control and validation. The approach is based on the description of system processing terms of an abstraction hierarchy of states of knowledge. The states of knowledge are organized along one dimension which corresponds to the extent to which the concepts are expressed in terms of the system inouts or in terms of the system response. Thus organized, the useful states form a generally triangular shape with the sensors and effectors forming the lower two vertices and the full evaluated set of courses of action the apex. Within the triangle boundaries are numerous processing paths which shortcut the detailed processing, by connecting incomplete levels of analysis to partially defined responses. Shortcuts at different levels of abstraction include reflexes, sensory motor control, rule based behavior, and satisficing. This approach was used in the design of a real time tactical decision aiding system, and in defining an intelligent aiding system for transport pilots.

Schudy, Robert↗

DOE OSTI2024

Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2001

Techniques for Improving Pilot Recovery from System Failures

This project examined the application of intelligent cockpit systems to aid air transport pilots at the tasks of reacting to in-flight system failures and of planning and then following a safe four dimensional trajectory to the runway threshold during emergencies. Two studies were conducted. The first examined pilot performance with a prototype awareness/alerting system in reacting to on-board system failures. In a full-motion, high-fidelity simulator, Army helicopter pilots were asked to fly a mission during which, without warning or briefing, 14 different failures were triggered at random times. Results suggest that the amount of information pilots require from such diagnostic systems is strongly dependent on their training; for failures they are commonly trained to react to with a procedural response, they needed only an indication of which failure to follow, while for 'un-trained' failures, they benefited from more intelligent and informative systems. Pilots were also found to over-rely on the system in conditions were it provided false or mis-leading information. In the second study, a proof-of-concept system was designed suitable for helping pilots replan their flights in emergency situations for quick, safe trajectory generation. This system is described in this report, including: the use of embedded fast-time simulation to predict the trajectory defined by a series of discrete actions; the models of aircraft and pilot dynamics required by the system; and the pilot interface. Then, results of a flight simulator evaluation with airline pilots are detailed. In 6 of 72 simulator runs, pilots were not able to establish a stable flight path on localizer and glideslope, suggesting a need for cockpit aids. However, results also suggest that, to be operationally feasible, such an aid must be capable of suggesting safe trajectories to the pilot; an aid that only verified plans entered by the pilot was found to have significantly detrimental effects on performance and pilot workload. Results also highlight that the trajectories suggested by the aid must capture the context of the emergency; for example, in some emergencies pilots were willing to violate flight envelope limits to reduce time in flight - in other emergencies the opposite was found.

Pritchett, Amy R.↗

NASA NTRS1996

Intelligent Highway System

Under contract to the Texas Department of Transportation, AlliedSignal Technical Services developed the Transportation Guidance System (TransGuide) used in San Antonio, Texas. The system monitors the passage of traffic over the sensors embedded in the roadways and detects incidents. Control center operators are alerted to the occurrence of an accident and the area of the occurrence is highlighted on a map display. TransGuide incorporates technology AlliedSignal developed under various contracts to NASA at Goddard Space Flight Center, Johnson Space Center and Jet Propulsion Laboratory, including the design of ground control centers.

Source record↗

NASA NTRS1987

A situation-response model for intelligent pilot aiding

An intelligent pilot aiding system needs models of the pilot information processing to provide the computational basis for successful cooperation between the pilot and the aiding system. By combining artificial intelligence concepts with the human information processing model of Rasmussen, an abstraction hierarchy of states of knowledge, processing functions, and shortcuts are developed, which is useful for characterizing the information processing both of the pilot and of the aiding system. This approach is used in the conceptual design of a real time intelligent aiding system for flight crews of transport aircraft. One promising result was the tentative identification of a particular class of information processing shortcuts, from situation characterizations to appropriate responses, as the most important reliable pathway for dealing with complex time critical situations.

Schudy, Robert↗

DOE OSTI2021

Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2019

The NASA Urban Air Mobility Testbed Flight Research Aircraft

The National Aeronautics and Space Administration (NASA) is leading government, industry and academic research effort known as Urban Air Mobility (UAM). The UAM activity goal is to develop the technology needed to make possible an urban air transportation system that makes use of human-crewed and crewless vehicles using automation, artificial intelligence and other technologies to safely and efficiently air transport people and goods within an urban environment. The NASA Langley Research Center (LaRC) created a UAM Flight Research Testbed Aircraft from a Cessna LC40 general aviation aircraft. The testbed has updated digital avionics and research systems needed to conduct flight research. The aircraft has two separate autopilots; a standard Federal Aviation Ad-ministration (FAA) certified system, and a modified research autopilot. The research autopilot has modifications that allow increased authority and enhancements to allow more automation and artificial intelligence controls. A network of three research computers hosts software to research several activities including automated air traffic sense and avoid, ground collision avoidance and obstacle avoidance. These UAM research activities involve automation and artificial intelligence technologies developed at three NASA centers. The NASA Langley, NASA Armstrong, and NASA Ames Research Centers are working together to develop and test these UAM technologies. This paper provides details of the systems, capabilities and research projects of the UAM Testbed Research Aircraft.

Howell, Charles T.↗

DOE OSTI2021

Applications of Digital Twin System in a Smart City System with Multi-Energy

With the increasing number of electric vehicles (EV), the smart transportation system is becoming more closely related to the smart energy system in a smart city. However, because of the high complexity, dynamics, and large-scale of these two systems, it is challenging to study their operation problems systematically, especially in severe conditions, e.g., the 2021 winter Texas power traffic crisis. With artificial intelligence, federated learning, edge computing, and automatic control, a digital twin based smart city is proposed in this paper and it focuses on two applications: smart transportation and smart energy grid. Both systems are presented in a structured design manner with several components in the digital twin system, which also contains many real-implementations of different scenarios. The proposed digital twin of a smart city provides a potential clue for the problems mentioned above.

artificial intelligence↗

NASA NTRS1987

Mission planning and simulation via intelligent agents

A system that can operate from a flight manifest to plan and simulate payload preparation and transport via Shuttle flights is described. The design alternatives and the prototype implementation of the payload hardware and inventory tracking system are discussed. It is shown how intelligent agents can be used to generate mission schedules, and how, through the use of these intelligent agents, knowledge becomes separated into small manageable knowledge bases.

Gargan, Robert A., Jr.↗

NASA NTRS1977

OAST Space Theme Workshop 1976

Papers that provide a technical foundation including research and technology base candidates for each of six space themes - space power, space industrialization, search for extraterrestrial intelligence, exploration of the solar system, global service, and advanced transportation systems - are presented. The material is mainly intended for further use by workshop participants and NASA elements concerned with space research and technology. While the data presented do not represent official plans or positions, they are part of the process of evolving such plans and positions. The information contained reflects the efforts of workshop participants and should be an aid in the successful implementation and execution of the Agency's near- and far-term advanced technology program.

Sadin, S. R.↗

DOE OSTI2025

AI-assisted transport of radioactive ion beams

Beams of radioactive heavy ions allow researchers to study rare and unstable atomic nuclei, shedding light into the internal structure of exotic nuclei and on how chemical elements are formed in stars. However, the extraction and transport of radioactive beams rely on time-consuming expert-driven tuning methods, where hundreds of parameters are manually optimized. Here, in this study, we introduce a system that employs Artificial Intelligence (AI), specifically utilizing Bayesian Optimization, to assist in the transport process of radioactive beams. We apply our methodology to real-life scenarios showing advantages when compared with standard tuning methods. This AI-assisted approach can be extended to other radioactive beam facilities around the world to improve operational efficiency and enhance scientific output.

43 PARTICLE ACCELERATORS↗

NASA NTRS1990

Overview of error-tolerant cockpit research

The objectives of research in intelligent cockpit aids and intelligent error-tolerant systems are stated. In intelligent cockpit aids research, the objective is to provide increased aid and support to the flight crew of civil transport aircraft through the use of artificial intelligence techniques combined with traditional automation. In intelligent error-tolerant systems, the objective is to develop and evaluate cockpit systems that provide flight crews with safe and effective ways and means to manage aircraft systems, plan and replan flights, and respond to contingencies. A subsystems fault management functional diagram is given. All information is in viewgraph form.

Abbott, Kathy↗

NASA NTRS2016

Serious Gaming for Test & Evaluation of Clean-Slate (Ab Initio) National Airspace System (NAS) Designs

Incremental approaches to air transportation system development inherit current architectural constraints, which, in turn, place hard bounds on system capacity, efficiency of performance, and complexity. To enable airspace operations of the future, a clean-slate (ab initio) airspace design(s) must be considered. This ab initio National Airspace System (NAS) must be capable of accommodating increased traffic density, a broader diversity of aircraft, and on-demand mobility. System and subsystem designs should scale to accommodate the inevitable demand for airspace services that include large numbers of autonomous Unmanned Aerial Vehicles and a paradigm shift in general aviation (e.g., personal air vehicles) in addition to more traditional aerial vehicles such as commercial jetliners and weather balloons. The complex and adaptive nature of ab initio designs for the future NAS requires new approaches to validation, adding a significant physical experimentation component to analytical and simulation tools. In addition to software modeling and simulation, the ability to exercise system solutions in a flight environment will be an essential aspect of validation. The NASA Langley Research Center (LaRC) Autonomy Incubator seeks to develop a flight simulation infrastructure for ab initio modeling and simulation that assumes no specific NAS architecture and models vehicle-to-vehicle behavior to examine interactions and emergent behaviors among hundreds of intelligent aerial agents exhibiting collaborative, cooperative, coordinative, selfish, and malicious behaviors. The air transportation system of the future will be a complex adaptive system (CAS) characterized by complex and sometimes unpredictable (or unpredicted) behaviors that result from temporal and spatial interactions among large numbers of participants. A CAS not only evolves with a changing environment and adapts to it, it is closely coupled to all systems that constitute the environment. Thus, the ecosystem that contains the system and other systems evolves with the CAS as well. The effects of the emerging adaptation and co-evolution are difficult to capture with only combined mathematical and computational experimentation. Therefore, an ab initio flight simulation environment must accommodate individual vehicles, groups of self-organizing vehicles, and large-scale infrastructure behavior. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ab initio simulation environment is similar to online gaming environments in which player participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual player's active participation.

Allen, B. Danette↗