Human Autonomy Teaming: Principles and Applications
This talk describes the need for human autonomy teaming (HAT), the basic principles of HAT and the application of those principles in various domains.
multi-vehicle control↗
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Foundational Human-Autonomy Teaming Research and Development in Scalable Remotely Operated Advanced Air Mobility Operations: Research Model and Initial Work
To achieve the scalability envisioned for many Advanced Air Mobility (AAM) applications, uncrewed aerial system (UAS) concepts are being pursued with the goal of enabling fewer human operators to manage more increasingly autonomous vehicles. NASA’s Transformational Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) subproject has identified human-autonomy teaming (HAT) as a critical area of research required to support these operations. Under T3-RAM, the HAT Foundational Research Activity has been tasked with providing basic research to identify HAT and human-automation interaction (HAI) principles that can be used to achieve scalable multi-vehicle UAS operations. This paper first outlines a research model to produce ecologically relevant basic research, then contextualizes completed and planned research and development activities within this model. Proposed research threads are presented, along with their practical and theoretical implications.
Human-Autonomy Teaming↗
MPATH (Measuring Performance for Autonomy Teaming with Humans) Ground Control Station: Design Approach and Initial Usability Results
Envisioned future Advanced Air Mobility (AAM) operations will require a transition of aircraft command and control from onboard pilots to remote operators. The National Aeronautics and Space Administration (NASA) has developed a research ground control station (GCS) software called MPATH (Measuring Performance for Autonomy Teaming with Humans) to study the human factors of remote operators in a representative AAM environment, where small uncrewed aerial systems (sUAS; simulated or real) act as surrogates for larger AAM aircraft. A primary focus of the research being conducted with the MPATH GCS is scalability (i.e., one human managing multiple vehicles). MPATH has demonstrated to be a useful capability for human factors research and remote operations. Two initial usability studies and a multi-vehicle control assessment were recently conducted, and usability data and operator feedback were collected. Generally, participants rated MPATH high on usability and interface quality, whereas information quality was rated slightly lower. These results were supported by specific feedback. Several generalized GCS design recommendations are proposed based on the results and feedback from these flight activities. Future updates to MPATH will incorporate the proposed recommendations. In practice, these recommendations could be used by any GCS software designer or developer to promote usability and safety.
Advanced Air Mobility↗
Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations
An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes.
Human-Autonomy Teaming↗
Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations
An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes
Human-Autonomy Teaming↗
The Distributed Spacecraft Autonomy (DSA)
NASA’s Distributed Spacecraft Autonomy (DSA) Project developed and demonstrated software to enhance multi-spacecraft mission adaptability, efficiently allocate tasks between spacecraft using ad-hoc networking, and enable human-swarm commanding of Distributed Space Missions.
Autonomy Distributed Space Missions↗
NASA In-Situ Resource Utilization (ISRU) Pilot Excavator (IPEX) Digital Twin Autonomy Challenge for Universities
The Lunar Autonomy Challenge is a competition for university students to develop algorithms to autonomously control systems in a realistic lunar environment.
In Situ Resource Utilization (ISRU)↗
Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic
Here, this article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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↗
Behavior, Energy, Autonomy, Mobility Modeling Framework
The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual's cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within-day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing.
Bae, Sangjae↗
Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0
The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.
Lazarus, Jessica↗
ALFA Shared Autonomy Manipulation Data with a Seabotix vLBV300
This report outlines marine field demonstrations for manipulation tasks with a semi-Autonomous Underwater Vehicle (sAUV). The vehicle is built off a Seabotix vLBV300 platform with custom software interfacing it with the Robot Operating System (ROS). The vehicle utilizes an inertial navigation system available from Greensea Systems, Inc. based on a Gladiator Landmark 40 IMU coupled with a Teledyne Explorer Doppler Velocity Log to perform station keeping at a desired location and orientation. We performed two marine trials with the vehicle: a near-shore shared autonomy manipulation trial and an offshore attempted intervention trial. These demonstrations were designed to show the capabilities of our sAUV system for inspection and basic manipulation tasks in real marine environments.
16 TIDAL AND WAVE POWER↗
Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research
Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility
This report summarizes the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy) and its application to simulating plug-in electric vehicle (PEV) mobility, energy consumption, and spatiotemporal charging demand. BEAM is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). We apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. We then explore the impact of a variety of common modeling assumptions in the literature regarding charging infrastructure availability and driver behavior. We find that accurately reproducing observed charging patterns requires an explicit representation of spatially disaggregated charging infrastructure as well as a more nuanced model of the decision to charge that balances tradeoffs people make with regards to time, cost, convenience, and range anxiety.
33 ADVANCED PROPULSION SYSTEMS↗
Human Factors Considerations for Anticipated Uses of Autonomy in Advanced Reactor Technologies
The objective of this report is examine anticipated advanced reactor designs that incorporate high levels of autonomy in operation of advanced reactor systems or facilities, and to begin identifying the human factors engineering (HFE) considerations relevant to those designs. The insights gained will help identify areas where U.S. Nuclear Regulatory Commission (NRC) may need to develop or update review guidance to adequately address HFE considerations essential for safe operation. The adequacy of existing guidance, along with recommendations for updates or new guidance, may be addressed in future reports. We use the term “landscape analysis” to describe the review, analysis, and characterization of publicly available information on the anticipated future state of the industry regarding particular technologies or operational concepts. This landscape analysis considers the potential range of advanced reactor automation concepts that may be used for monitoring and controlling nuclear systems and facilities, including the potential for highly automated, near autonomous, or fully autonomous operation of advanced nuclear reactors.
99 - GENERAL AND MISCELLANEOUS↗
Autonomy in space navigation
The potential development of autonomous onboard navigation systems for future space missions is discussed. Motivations for autonomy in the near-earth satellite program and in deep-space navigation are examined, emphasizing lower mission-operation costs for the first, and reduction of ground-system complexity as well as encounter accuracy for the latter. Uses of navigation parameters by the Information Adaptive System, including sensor-pointing computation and image correction, are noted, as are the accuracy parameters of the NAVSTAR Global Positioning System. The role of autonomous onboard navigation in contact exploration of Neptune's and Uranus's satellites as well as in such extreme-high-velocity encounters as those with retrograde comets is investigated. The Automated Optical Navigation program is described
Jordan, J. F.↗
Space station automation and autonomy - Advantages and problems
Design guidelines and functional systems being considered in the process of defining the configuration of the automated systems for a manned space station are outlined. The requirements are dependent on life-cycle costing and will set the necessary level of automation, as well as autonomy from outside commands. Fault protection routines have been largely devised according to successful programming on the Voyager spacecraft. An analysis is still needed of the housekeeping functions, including human necessities, machine functions, and mission objectives. A data base will result, defining the functions that have historically been delegated to either man or machine. Care must be taken to coordinate and document stationkeeping functions that might interface with mission functions. A data management system that is flexible with regards to changing mission objectives and to the MTBF factors, which will determine the level of technology to be used is required. Expert systems will be integrated into the automation to guide the machines in problem solving, including ensuring adequate management of the battery subsystem.
Carlisle, R. F.↗
Space platforms and autonomy
Potential applications for autonomous space platforms (SP) are discussed. The platforms are assumed to have long in-service lifetimes and therefore be flexible as to configuration modification and payload changeout. Higher degrees of autonomy, particularly from ground control, are made possible because of the rapid increase of microprocessor power and artificial intelligence advances. Functioning independently, the platforms are to rely only on periodic refurbishment visits by, e.g., the Orbiter. The Manned Space Station (MSS) will be the most complex structure, involving multifacted man-machine interfaces. The SP can be subsystems of the MSS (or other platforms), handling communications enunciation, data acquisition, analysis and telemetry, fault detection and isolation, systems monitoring and control, etc. The SP adopted will depend in all cases on costs vs benefits analyses to determine the worth of removing the function(s) from direct, regular human intervention.
Easter, R. W.↗