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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Using Desis and EO-1 Hyperion Reflectance Time Series for the Assessment of Vegetation Traits and Gross Primary Production (GPP)

This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400-2400 nm) reflectance collection over vegetated flux sites to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using VNIR reflectance from EO-1 Hyperion with DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function.

DESIS↗
NASA NTRS2022

Reservoir Assessment Tool 2.0: Stakeholder driven improvements to satellite remote sensing based reservoir monitoring

In light of the rapidly increasing regulation of rivers due to planned and constructed reservoirs, monitoring reservoir operations has become very crucial. The Reservoir Assessment Tool (RAT) framework was developed to monitor reservoir operations globally, using hydrological modeling and satellite observations. With feedback from stakeholders, improvements in the RAT framework are demonstrated in this study using the Mekong River Basin as an example. A novel multi-sensor reservoir area mapping technique was developed using complementary strengths of optical and SAR sensors at a 1-5 day temporal resolution, allowing the quantification of sub-weekly reservoir operations. Additionally, the skill of radar altimeters in the RAT framework was tested using the Jason-3 altimeter. Using in-situ data from three Thai reservoirs in the Mekong Basin, consistent improvements were observed as compared to the original RAT framework. A functionality for visualizing forecasted outflow was also added using historically inferred reservoir operations or stakeholder-driven target storage expectations.

Reservoirs↗
NASA NTRS2022

Local Scale (3-M) Soil Moisture Mapping Using SMAP and Planet Superdove

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

soil moisture↗
NASA NTRS2022

3D-CHESS: Decentralized, Distributed, Dynamic, and Context-aware Heterogeneous Sensor Systems

This paper describes the objectives and current status of the 3D-CHESS project which aims to demonstrate a new Earth observing strategy based on a context-aware Earth observing sensor web. This sensor web consists of a set of nodes with a knowledge base, heterogeneous sensors, edge computing, and autonomous decision-making capabilities. Context awareness is defined as the ability for the nodes to gather, exchange, and leverage contextual information (e.g., state of the Earth system, state and capabilities of itself and of other nodes in the network, and how those states relate to the dy- namic mission objectives) to improve decision making and planning. The current goal of the project is to demonstrate proof of concept by comparing the performance of a 3D- CHESS sensor web with that of status quo architectures in the context of a multi-sensor inland hydrologic and ecologic monitoring system.

David, Cedric H.↗
NASA NTRSDate not supplied

The GEOS Neural Network Retrieval (NNR) for Multi-spectral AOD

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from multiple sensors. In this presentation, I will present recent updates to the GEOS-5 NNR for estimation of spectral AOD from MODIS and VIIRS, and the potential for multi-channel AOD assimilation to provide constraints on aerosol composition.

Patricia Castellanos↗
NASA NTRSDate not supplied

An Oveview of TROPESS Data Products and Services at the NASA GES DISC

The TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project generates Earth System Data Records (ESDRs) of ozone, and other atmospheric constituents (CH4,CO, H2O, HDO, NH3, PAN and temperature) by processing data from multiple satellites through a common retrieval algorithm and ground data system. Satellite Level-1B input data used in generating the TROPESS L2 data products include CrIS NOAA-20 (JPSS-1), CrIS SNPP, AIRS Aqua, OMI Aura, and TROPOMI S5P. The common retrieval framework is known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) science data processing system (MUSES-SDPS). Several of the TROPESS data products are now available from the NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) for users to download. In this presentation we provide an overview of the various TROPESS data products. These data products can be divided into the following Forward Stream types: Standard Products, Summary Products, and Full-Archival Products. Standard Products are for users that are doing full analysis with avenging kernel and covariance corresponding to retrieved vertical profiles. Summary products have a smaller file size and are more convenient for first-look and rapid analysis, include total and partial columns, as well as column averaging kernels. The Full-Archival Products will contain all information used in creating the data. TROPESS also creates Special Products, provided on an as-needed and as-available basis to support NASA field missions and individual-investigator requests over specific regions. Eventually, TROPESS will also produce and deliver a set of Reanalysis Stream products. Data at the GES DISC are being transitioned into the "Cloud". This will allow users with "Cloud” access to perform data analysis directly on the data without downloading the data to their system. Services, such as subsetting and data visualization, will also be provided for TROPESS data products at the GES DISC.

James Johnson↗
NASA NTRS2023

A Nowcasting Approach for Low Earth Orbit Hyperspectral Infrared Soundings Within the Convective Environment

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to six hours to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the Contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.

North America↗
NASA NTRSDate not supplied

Accelerating the Deployment of Space-Based Quantum Sensors

Quantum sensors rely on quantum mechanical properties, such as wave-particle duality, atomic energy levels, or entanglement, to measure physical, optical, or electromagnetic information from a target. Examples include Rydberg sensors, nitrogen-vacancy magnetometers, HOM (Hong-Ou-Mandel) interferometers, and SQUIDs (Superconducting Quantum Interference Devices). In general, as quantum sensors rely on interrogation at the atomic and photonic level rather than on classical mechanical measurements, these technologies promise advantages over their traditional counterparts in sensitivity, accuracy, and SWaP (Size, Weight, and Power). Conceptually, the use of quantum sensors unlocks the new phenomenology of sensor entanglement. While an ultra-low TRL (Technology Readiness Level) prospect for most sensor types, multi-sensor entanglement is a high risk, high reward strategy with a potential for exquisite sensitivity and SWaP improvements entirely inaccessible to classical sensors.

Grace Caroline Wusk↗
NASA NTRSDate not supplied

Improving the Time Resolution of Hyper-Spectral Infrared Sounding Observations With Trajectory Enhancement

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant potential for characterizing the complex evolution of thermodynamic environments favorable for convective initiation and ongoing convection. The snapshots at fixed local times are unable to provide the temporal resolution needed to resolve the rapidly evolving convective environment. A novel methodology using trajectory modeling coupled with satellite soundings was developed to create proximity soundings near NCEI Storm Events to investigate differences in severe weather environments (Kalmus et al., 2019, https://doi.org/10.1175/MWR-D-18-0055.1). This methodology was recently extended to entire satellite swaths forward in time up to six hours into the future and was evaluated during NOAA’s Hazardous Weather Testbed (HWT) in a quasi-operational setting (Kahn et al., 2023, https://doi.org/10.1175/WAF-D-22-0204.1). This methodology is based on parcel forward-trajectory calculations from the satellite observing time to recreate future soundings of temperature and moisture at regularly gridded intervals in space and time. Using coincident Multi-Radar Multi-Sensor (MRMS) rainfall estimates, we show that convective available potential energy (CAPE) is increased and convective inhibition (CIN) is decreased at times and locations where convection initiated. This methodology was evaluated with ERA5 data sampled to mimic the satellite observing swaths (Richardson et al., 2023, https://doi.org/10.5194/egusphere-2023-97). Approximately 60–90% of the temporal and spatial variability in temperature and humidity is explained by parcel advection using trajectory modeling. This method will be applied to the multi-decadal LEO satellite record and could be used to examine capabilities of future satellite missions such as the GeoXO mission.

Emily Berndt↗
NASA NTRS2024

PACE Technical Report Series, Volume 12: The PACE Level 1C data format

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will make global ocean color and atmospheric measurements to provide extended data records of ocean ecology and global biogeochemistry, along with polarimetric measurements for advanced observations of aerosols, clouds and the ocean. PACE will contain three instruments: the primary Ocean Color Instrument (OCI), and two multi-angle polarimeters (MAPs). The latter instruments are contributed under a ‘Do-No-Harm’ (to the rest of the PACE mission) principle, and the PACE Science Data Processing System (SDPS) is only required to produce Level-1b (geolocated radiances with calibration applied) data without performance requirements. However, there is a strong desire to produce data in a format that merges the disparate spatial resolutions, viewing geometry and sampling nature of the three instruments. Our terminology for this format is Level 1c (L1C). This format will be an input to Level 2 algorithms produced from standalone MAP instrument observations, or from algorithms employing multi-sensor fusion. Creating the L1C format has several components. This includes choice of projection method, the means by which multi-angle views are properly incorporated into that projection (‘aggregation’) the means to represent wavelength and light polarization state, the selection of data to be included within the L1C file, and the handling of ancillary data either required for L1C file generation or needed in that format for L2 processing.

PACE↗
NASA NTRSDate not supplied

The West-Coast Hyperspectral Microwave Sensor Intensive Experiment (WHyMSIE): A Prototype for A PBL Mission of Missions

We present an overview of the 2024 West-Coast Hyperspectral Microwave Sensor Intensive Experiment(WHyMSIE). WHyMSIE is a joint NASA-NOAA multi-sensor airborne experiment, embracing passive and active sensors from the Program of Record (PoR) along with novel technology funded through the NASA ESTO Instrument Incubation Program. At the core of this effort is the demonstration of the Conical Scanning Millimeter-wave Imaging Radiometer Hyperspectral (CoSMIR-H) instrument, a PBL DSI funded effort to develop hyperspectral sounding capability in the thermal microwave domain finalized to improved temperature and water vapor soundings in the Earth’s Planetary Boundary Layer (PBL). An overview of the field campaign design, instrument payload and validation plan is presented here.

Planetary Boundary Layer↗
NASA NTRS2024

Meteorology Modulates the Impact of GCM Horizontal Resolution on Underestimation of Midlatitude Ocean Wind Speeds

We utilize ocean 10-m wind speed (U 10m ) from the microwave Multi-sensor Advanced Climatology data set to examine the coupling between convective cloud and precipitation processes, synoptic state, and U 10m and to evaluate the representation of U 10m in global climate models (GCMs). We find that midlatitude U 10m is underestimated by GCMs relative to observations. We examine two potential mechanisms to explain this model behavior: cold pool formation in cold air outbreaks (CAOs) associated with downdrafts that enhance U 10m and sea surface temperature (SST) gradients affecting U 10m through thermally forced surface winds at regional scales. When the effects of the CAO index (M) and SST gradients on U 10m are accounted for, a relationship between GCM horizontal resolution and U 10m appears. The strongest correlation between resolution and U 10m is over the western boundary currents characterized by frequent CAOs atop strong SST gradients which drives the strongest surface fluxes on Earth.

surface wind speed↗
NASA NTRS2025

Hilo Bay Water Resources: Monitoring Water Quality in Hilo Bay, Hawaii to Support Future Community Planning

Designated as an impaired body of water by both state and federal water quality standards, Hilo Bay, Hawaiʻi is highly susceptible to brown water, a condition where the water becomes murky and is associated with excess levels of bacteria, contaminants, and nutrients. A breakwater in Hilo Bay, which was established to protect Hilo town from tsunamis, interferes with water circulation and prolongs the presence of brown water in the bay. The State of Hawaiʻi issues brown water advisories (BWAs) following flash flood warnings, sewage spills, and other events to indicate a public health concern for those who use Hilo Bay for recreation, cultural purposes, and fishing. Due to the elevated public health risk and ecosystem disturbance that brown water poses to Hilo Bay, we partnered with the Hawaiʻi County Office of Sustainability, Climate, Equity, and Resilience (OSCER) to examine the feasibility of using Earth observations (EO) to monitor water quality in the Hilo Bay region. We leveraged data from Sentinel-2 Multispectral Instrument (MSI), Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, and Aqua and Terra Moderate Resolution Imagine Spectroradiometer (MODIS) instruments to identify and assess spatial and temporal patterns of two main water quality parameters, turbidity and chlorophyll-a, during BWAs. We used the Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine to process EO data and generate water quality maps and time series. Our study found that increased turbidity levels can be identified by EO data during BWAs. In addition, our map products indicated the presence of several turbidity plumes along the coast, with the highest concentration of turbidity found within Hilo Bay. While chlorophyll-a levels were relatively flat within our study region during BWAs, we found that regional chlorophyll-a patterns could be derived from MODIS chlorophyll-a data in NASA Worldview. Our study’s multi-sensor approach provided valuable insights for how water quality in the Hilo Bay region can be monitored in the future.

remote sensing↗
NASA NTRSDate not supplied

Improved Assessment of Recent Trends in NOx and VOC Emissions and Ozone Production Sensitivity Regimes Using Satellite Data

This presentation highlights results from a NASA Aura Science Team and Atmospheric Composition Modeling and Analysis Program (ACMAP) project which study the capability to observe and model trends in ozone (O3) production regimes using spaceborne sensors. Ultraviolet– visible (UV–Vis) tropospheric column satellite retrievals of formaldehyde (HCHO) (a proxy for volatile organic compound [VOC] reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides [NOx]) are frequently used to investigate the sensitivity of O3 production to emissions of NOx and VOCs. There are challenges that come from using satellite-derived ratios of HCHO and NO2 (FNR) to study O3 production sensitivity with the largest uncertainties associated with specific spaceborne sensor’s retrieval biases and errors. This study quantifies the differences and improvements in satellite retrievals of O3 production sensitivity regimes using FNRs when moving from legacy polar orbiting satellites such as the Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) onboard the NASA/NOAA Suomi-NPP platform to newer, higher spatiotemporal resolution satellite sensors TROPOspheric Monitoring Instrument (TROPOMI) and eventually the recently launched NASA geostationary sensor Tropospheric Emissions: Monitoring of Pollution (TEMPO). Furthermore, we investigate how using retrievals of NO2 and HCHO from these different satellites to constrain model predictions impacts the ability to accurately simulate O3 chemistry including chemical production regimes. To this end, we have conducted inverse model simulations, using the WRF-CMAQ-DDM data assimilation system at 12 km × 12 km, to constrain emissions of NOx and VOCs over the contiguous United States (CONUS) when assimilating OMI and TROPOMI retrievals of NO2 and HCHO. Two advantages of this are that we a) account for each satellite’s errors/biases in the emission estimation and b) update the prior profile to ensure that only radiance information is used for optimizing the emissions. This presentation will demonstrate: a) the varying accuracy of different satellite retrieved FNRs and ability to capture known sub-annual emission trends (e.g., seasonal, weekend/weekday) and emission anomalies during the COVID-19 lockdown of 2020, b) the differences and improvements in top-down emission estimates of NOx and VOCs when constrained by newer satellite sensors compared to legacy systems, and c) multi-sensor optimized emission estimates of summer-time NOx and VOCs between 2019-2021.

Data↗
NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
DOE OSTI2024

Safe Operations at Roadway Junctions: Intelligent Roadway Infrastructure as Functional Interlocking

Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2025

Infrastructure-Based Cooperative Perception at a Traffic Intersection: Overview and Challenges

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. About one-quarter of traffic fatalities and about one-half of all traffic injuries in the United States happen at traffic intersections . Effective management of these intersections is important to ensure safety and efficiency of all users - vehicles, pedestrians, cyclists, and vulnerable road users (VRUs). With advancements in sensor perception technologies such as radar, light detection and ranging (lidar), and cameras, traffic intersections are developing into dynamic and data-rich environments. By using these data to create a real-time digital twin, we can enable real-time data-driven decision making and a range of applications such as sharing perception information to connected vehicles (CVs) and connected autonomous vehicles (CAVs), safety affirmative signaling, and curb optimizing to improve efficiency and enhance safety.This paper presents an overview of the concept and examines the challenges involved in implementing an infrastructure-based cooperative perception engine at a traffic intersection. In addition to outlining the physical components, this study also addresses important challenges involved in a multi-sensor system. We present results from deploying the National Renewable Energy Laboratory's (NREL's) Infrastructure Perception and Control (IPC) mobile trailer at a traffic intersection in the city of Colorado Springs, Colorado, USA that employed multiple radars and lidars to capture the data. This study provides necessary practical learning for the Cooperative Driving Automation (CDA) and traffic engineering communities for next-generation infrastructure-based cooperative perception that promises improvements in signal control for optimized traffic flow, among other applications, and documents findings for ongoing research and development efforts in other areas.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗