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NASA NTRS2012

Global Characterization and Monitoring of Forest Cover Using Landsat Data: Opportunities and Challenges

The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this article, we describe the methods to create global products of forest cover and cover change at Landsat resolutions. Nevertheless, there are many challenges in ensuring the creation of high-quality products. And we propose various ways in which the challenges can be overcome. Among the challenges are the need for atmospheric correction, incorrect calibration coefficients in some of the data-sets, the different phenologies between compilations, the need for terrain correction, the lack of consistent reference data for training and accuracy assessment, and the need for highly automated characterization and change detection. We propose and evaluate the creation and use of surface reflectance products, improved selection of scenes to reduce phenological differences, terrain illumination correction, automated training selection, and the use of information extraction procedures robust to errors in training data along with several other issues. At several stages we use Moderate Resolution Spectroradiometer data and products to assist our analysis. A global working prototype product of forest cover and forest cover change is included.

Global↗

NASA NTRS2024

Improvements to GCAS Visualization Functionality

Analysis of communication systems is crucial to NASA objectives, whether located here on Earth or on a cosmic scale. The Glenn Research Center Communication Analysis Suite (GCAS) is software designed for analysis of communication systems throughout the solar system. This suite includes web-based visualization software that is used to simulate the data generated from the GCAS MATLAB® (The MathWorks, Inc.) capability. This report focuses on recent additions to the visualization portion of GCAS, including the motivation and methodology behind them. Notable additions include improved camera controls, such as novel first- and third-person settings, methods for displaying pertinent analysis information, finer control over timing mechanisms, and automated features designed to improve efficiency and memory usage.

Visualization↗

DOE OSTI2022

What you get is not always what you see—pitfalls in solar array assessment using overhead imagery

Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Here our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.

14 SOLAR ENERGY↗

DOE OSTI2021

Containerization in ATLAS Software Development and Data Production

The ATLAS experiment's software production and distribution on the grid benefits from a semi-automated infrastructure that provides up-to-date information about software usability and availability through the CVMFS dis-tribution service for all relevant systems. The software development process uses a Continuous Integration pipeline involving testing, validation, packag-ing and installation steps. For opportunistic sites that can not access CVMFS, containerized releases are needed. These standalone containers are currently created manually to support Monte-Carlo data production at such sites. In this paper we will describe an automated procedure for the containerization of AT-LAS software releases in the existing software development infrastructure, its motivation, integration and testing in the distributed computing system.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

Ramapriyan, H.↗

NASA NTRS1980

Preliminary design of graphite composite wing panels for commercial transport aircraft

Subjectively assessed practical and producible graphite/epoxy designs were subjected to a multilevel screening procedure which considered structural functions, efficiency, manufacturing and producibility, costs, maintainability, and inspectability. As each progressive screening level was reviewed, more definitive information on the structural efficiency (weight), manufacturing, and inspection procedures was established to support the design selection. The configuration features that enhance producibility of the final selected design can be used as a generic base for application to other wing panel designs. The selected panel design showed a weight saving of 25 percent over a conventional aluminum design meeting the same design requirements. The estimated cost reduction in manufacturing was 20 percent, based on 200 aircraft and projected 1985 automated composites manufacturing capability. The panel design background information developed will be used in the follow-on tasks to ensure that future panel development represents practical and producible design approaches to graphite/epoxy wing surface panels.

Byers, B. A.↗

DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗

DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗

NASA NTRS2013

Pilot and Controller Evaluations of Separation Function Allocation in Air Traffic Management

Two human-in-the-loop simulation experiments were conducted in coordinated fashion to investigate the allocation of separation assurance functions between ground and air and between humans and automation. The experiments modeled a mixed-operations concept in which aircraft receiving ground-based separation services shared the airspace with aircraft providing their own separation service (i.e., self-separation). Ground-based separation was provided by air traffic controllers without automation tools, with tools, or by ground-based automation with controllers in a managing role. Airborne self-separation was provided by airline pilots using self-separation automation enabled by airborne surveillance technology. The two experiments, one pilot-focused and the other controller-focused, addressed selected key issues of mixed operations, assuming the starting point of current-day operations and modeling an emergence of NextGen technologies and procedures. In the controller-focused experiment, the impact of mixed operations on controller performance was assessed at four stages of NextGen implementation. In the pilot-focused experiment, the limits to which pilots with automation tools could take full responsibility for separation from ground-controlled aircraft were tested. Results indicate that the presence of self-separating aircraft had little impact on the controllers' ability to provide separation services for ground-controlled aircraft. Overall performance was best in the most automated environment in which all aircraft were data communications equipped, ground-based separation was highly automated, and self-separating aircraft had access to trajectory intent information for all aircraft. In this environment, safe, efficient, and highly acceptable operations could be achieved for twice today's peak airspace throughput. In less automated environments, reduced trajectory intent exchange and manual air traffic control limited the safely achievable airspace throughput and negatively impacted the maneuver efficiency of self-separating aircraft through high-density airspace. In a test of scripted conflicts with ground-managed aircraft, flight crews of self-separating aircraft prevented separation loss in all conflicts with detection time greater than one minute. In debrief, pilots indicated a preference for at least five minute's alerting notice and trajectory intent information on all aircraft. When intent information on ground-managed aircraft was available, self-separating aircraft benefited from fewer conflict alerts and fewer required deviations from trajectory-based operations.

Wing, David↗

DOE OSTI2021

A Modularized Urban Scale Building Energy Modeling Framework Designed with An Open Mind

In recent years, physics-based building energy modeling (BEM) has started being used to evaluate the performance of buildings in the context of connected communities and on an urban scale to study their aggregated energy use, interactions, and impacts on the energy supply infrastructure and environment. The development of urban-scale BEM solutions needs extensive effort. Existing attempts tend to focus on different aspects of BEM on an urban scale, such as collecting as-built building data from different information sources, integrating geometry modeling with geographic information systems (GISs), representing operational and occupancy profiles, automating workflow, processing and visualizing the results, and conducting large-scale simulations. Urban-scale BEM development would benefit from multi-disciplinary research areas and from an open platform to adopt advancements on data sources and tools. For these purposes, this research proposes a modularized bottom-up model creation and simulation framework that is built on the state-of-the-art BEM tools and can accommodate different building stock data. This framework uses a standardized schema to describe building design and operational characteristics, and it can be instantiated from different building survey datasets with heterogeneous structures. The paper demonstrates how thousands of surveyed buildings from the 2012 U.S. Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) were one-to-one converted to EnergyPlus models through the schema and the model generation process, then simulated with distributed computing, and their results are summarized.

Lei, Xuechen↗

NASA NTRS1992

NASA technology transfer network communications and information system: TUNS user survey

Applied Expertise surveyed the users of the deployed Technology Utilization Network System (TUNS) and surveyed prospective new users in order to gather background information for developing the Concept Document of the system that will upgrade and replace TUNS. Survey participants broadly agree that automated mechanisms for acquiring, managing, and disseminating new technology and spinoff benefits information can and should play an important role in meeting NASA technology utilization goals. However, TUNS does not meet this need for most users. The survey describes a number of systematic improvements that will make it easier to use the technology transfer mechanism, and thus expedite the collection and dissemination of technology information. The survey identified 26 suggestions for enhancing the technology transfer system and related processes.

Source record↗

NASA NTRSDate not supplied

A Human-In-The-Loop Simulation for Urban Air Mobility in the Terminal Area

In this presentation we propose a human-in-the-loop experiment to study the potential impact of human engagement in tactical mitigation of delay in terminal area air taxi operations. The air taxi operations being considered herein is an advanced air transportation concept called Urban Air Mobility (UAM). The UAM concept aims to support not only air taxi operations, but also package delivery and emergency response among other use cases. The key innovation over current air transportation lies with the introduction of autonomous aircraft and autonomous air traffic management systems. Development of the UAM system will include transitional midterm phases where some operational services will be provided by a mixture of automation and human actors. Midterm operations present a unique challenge, since the scope of responsibility of automated systems is largely undefined, suggesting the need for direct human participation with little to inform how much human intervention is necessary. Here we assume that traffic management responsibilities require coordination between human actors and automated systems and focus on arrival flows for midterm operations. In the proposed human-in-the-loop simulation, virtual UAM traffic is strategically deconflicted by a Provider of Services for UAM at departure, then tactically managed by a human at the arrival facility. Generated traffic consists of UAM participants flying in UAM exclusive airspace structures, thus isolated from traditional traffic. The human operator is tasked with managing spacing of arrival traffic and executing speed adjustments as deemed necessary. We propose the investigation of three levels of automation assistance: 1) no assistance; 2) spacing violation detection; 3) spacing violation detection and speed adjustment recommendations. Quantitative measures like throughput and delay are used to assess the human's capacity for accommodating airborne delays. Qualitative evaluations such as surveys and open-ended feedback are used to gain insight into human factors. These factors could introduce additional capacity constraints on traffic, independent of physical or technical constraints. Although findings for this study will not be reported as the study has not yet been executed, we conclude with potential outcomes informed by previous simulations in the literature and suggestions for the structure and procedures of midterm human-automation air traffic management.

UAM↗

NASA NTRS2012

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.

Ong, James C.↗

NASA NTRS2017

Evolution of NASA's Earth Science Digital Object Identifier Registration System

NASA's Earth Science Data and Information System (ESDIS) Project has implemented a fully automated system for assigning Digital Object Identifiers (DOIs) to Earth Science data products being managed by its network of 12 distributed active archive centers (DAACs). A key factor in the successful evolution of the DOI registration system over last 7 years has been the incorporation of community input from three focus groups under the NASA's Earth Science Data System Working Group (ESDSWG). These groups were largely composed of DOI submitters and data curators from the 12 data centers serving the user communities of various science disciplines. The suggestions from these groups were formulated into recommendations for ESDIS consideration and implementation. The ESDIS DOI registration system has evolved to be fully functional with over 5,000 publicly accessible DOIs and over 200 DOIs being held in reserve status until the information required for registration is obtained. The goal is to assign DOIs to the entire 8000+ data collections under ESDIS management via its network of discipline-oriented data centers. DOIs make it easier for researchers to discover and use earth science data and they enable users to provide valid citations for the data they use in research. Also for the researcher wishing to reproduce the results presented in science publications, the DOI can be used to locate the exact data or data products being cited.

DOI; digital object identifier; mappin↗

NASA NTRSDate not supplied

Human Capabilities Assessments for Autonomous Missions: A Multi-Team Research Effort to Reduce Risk in the Human-System Integration Architecture for Future Deep-Space Missions

In future exploration missions beyond low earth-orbit, crew will have to execute complex operations and respond to off-nominal events, without real-time support from Mission Control. It is anticipated that increased reliance on automated systems, including human-centric vehicle and information architecture, will need to be designed to support the crew; increased risk to performance, health, and safety may occur if these are not implemented appropriately. The Human Factors and Behavioral Performance Element (HFBP) in the NASA Human Research Program supports research to characterize and mitigate such human health and performance risks, including the Risk of Adverse Outcome Due to Inadequate Human Systems Integration Architecture (HSIA). The HSIA risk addresses the integration of onboard capability and the crew roles and responsibilities necessary to enable the crew to respond effectively and efficiently in the increasingly autonomous mission operations environment. In 2017, HFBP released the “Human Capabilities Assessments for Autonomous Missions” (HCAAM) research topic to address HSIA related questions. HCAAM is a major NASA research effort that has assembled a multidisciplinary team from seven institutions to work closely with design and engineering efforts on research towards developing and refining human performance standards, guidelines and automation tools. The scientific focus is on quantitative assessment of human capabilities relevant to future deep-space missions during which earth/spacecraft communication is so delayed and intermittent that the crew must be able to function autonomously. The integrated strategy of the HCAAM project characterizes human capabilities and limitations related to potential performance decrements during long duration exploration mission spaceflight as relevant to both routine and complex task performance; defines system characteristics that reduce the likelihood or impact of potential decrements in human performance capabilities; performs integrated assessment of intelligent system responses within the context of an operational environment with relevant NASA tools, systems, and data structures in order to determine positive or negative interactions and validate recommended approaches; and proposes specific updates to existing standards and guidelines for inclusion in NASA handbooks for the design of future spacecraft intelligent systems that provide crew performance assessment/feedback, and to also serve as decision-support aids for the onboard crew (i.e., NASA-STD-3001, and NASA/SP-Human Integration Design Handbook (HIDH)). The scientific research vectors being addressed by the seven HCAAM teams include: - crew task performance (accuracy, efficiency) (crew + automation) - crew performance (accuracy, efficiency) - crew Situation Awareness - procedure design and multi-modal enhancement - concurrent tasking (mixed manual + some level of autonomy) - task handover - crew self-planning and time-lining - task design - trust in automation, real-time calibration - human multi-sensory feedback and guidance - human trust in on-board software-based intelligent assistants - virtual assistants The presentation will highlight plans and progress made in each of these research areas as well as the methods by which surrogate astronaut crews in the NASA JSC HERA spaceflight analog facility will function as human test subjects for all of the HCAAM research projects.

HCAAM VNSCOR↗

NASA NTRS2017

Measuring the Effectiveness of Human Autonomy Teaming

Human-Automation Teaming (HAT), is now recognized as a promising solution to the problems of humans managing increasingly complex work systems. A human-automation team can be defined as the interdependent coupling between one or more human operators and one or more autonomous systems requiring collaboration and coordination to accomplish system and task goals (e.g., Langan-Fox et al., 2009). In this conception, automated agents are considered team members that can operate at various levels of automation, be focused on one or more human-information-processing stages, and the interactions with human operators may be adaptable, adjustable or mixed initiative. We investigated some metrics for assessing HAT effectiveness in a demonstration of a HAT tool used by ground station operators in a Reduced Crew Operations project that was conducted at NASA Ames Human Automation Teaming Laboratory. In this paper, we focus on operator metrics of HAT effectiveness, specifically workload and operator behaviors.

measurements↗

NASA NTRSDate not supplied

A Human-In-The-Loop Simulation for Urban Air Mobility in the Terminal Area

In this paper researchers propose a human-in-the-loop experiment to study human performance when tasked with tactical deconfliction in terminal area air taxi operations. The air taxi operations being considered herein are an advanced air transportation concept called Urban Air Mobility (UAM). The UAM concept aims to support not only air taxi operations, but also package delivery and emergency response among other use cases. The key innovation over current air transportation lies with the introduction of highly automated aircraft and air traffic management systems. Development of the UAM system will include transitional midterm phases where some operational services will be provided by a mixture of automation and human actors. Midterm operations present a unique challenge, since the scope of responsibility of automated systems is largely undefined, suggesting the need for direct human participation with little to inform how much human intervention is necessary. Here it is assumed that traffic management responsibilities require coordination between human actors and automated systems and focus on arrival flows for midterm operations. In the proposed human-in-the-loop simulation, virtual UAM traffic is strategically deconflicted by a Provider of Services for UAM at departure, then tactically managed by a human at the arrival facility. Generated traffic consists of UAM participants flying in UAM exclusive airspace structures, thus isolated from traditional traffic. The human operator is tasked with managing spacing of arrival traffic and executing speed adjustments as deemed necessary. Researchers propose the investigation of three levels of automation assistance: 1) no assistance; 2) spacing violation detection; 3) spacing violation detection and speed adjustment recommendations. Quantitative measures like throughput and delay are used to assess the human's capacity for accommodating airborne delays. Qualitative evaluations such as surveys and open-ended feedback are used to gain insight into human factors. These factors could introduce additional capacity constraints on traffic, independent of physical or technical constraints. Although findings for this study will not be reported as the study has not yet been executed, the authors conclude with potential outcomes informed by previous simulations in the literature and suggestions for the structure and procedures of midterm human-automation air traffic management.

UAM↗

DOE OSTI2020

Corroborative Evaluation of the Real-World Energy Saving Potentials of InfoRich Eco-Autonomous Driving (iREAD) System

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing. These four approaches, spanning simulation and testing aspects, evaluate real-world fuel economy benefits with different ranges and resolutions. The large-scale simulations leverage an extensive real-world driving database to assess overall eco-driving benefits across a range of road network and driving scenarios. The real-world driving data are further leveraged to generate representative driving routes for deeper evaluation. Based on the representative routes, in-depth simulation relying on high-fidelity models investigates how different traffic scenarios can impact the eco-driving performance. The vehicle-in-the-loop setup reinforces the in-depth simulations by conducting tests with an actual vehicle operated on a chassis dynamometer; the measured energy savings were indeed found to agree with the in-depth simulation savings estimates. Finally, limited but representative road testing with the fully integrated vehicle will be conducted to demonstrate the eco-driving capability and conclude the overall evaluation regimen.

ARPA-E↗