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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Analysis of Platooning Trucks to Better Understand Dynamic Air Flow

Track testing has shown significant fuel-savings promise for truck platooning strategies but also raised unexpected questions about close following and long-distance following scenarios that could significantly impact savings realized in real-world conditions. The 2017 track test collaboration among NREL, Lawrence Berkeley National Laboratory, NRC Canada, Transport Canada, and others included onboard instrumentation to help the team gain a deeper understanding of the dynamic interaction between multiple vehicles. NREL's approach includes detailed data analysis for additional onboard sensors and J1939 CAN bus data from 2017 track test to investigate following truck air flow and turbulence changes to explain reduced savings at close following distances for the last vehicle in a platoon, define engine-cooling impacts of platooning position in different formations due to reduction of ram air through front grill and generate an understanding of a true in-use "baseline" with other vehicles on the highway. Initial data analysis indicates many of the data trends in wind angle, wind speed, and temperatures show a change in pattern for the closer following distances where fuel savings decrease for the following vehicles was also documented. This is encouraging in that the planned further analysis may yield the desired insights into the cause of the reduced savings. Once analyzed, light-duty vehicle and dynamic scenarios will help us refine the sensitivity of findings from the standard platooning scenarios.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2011

Final report : testing and evaluation for solar hot water reliability.

Solar hot water (SHW) systems are being installed by the thousands. Tax credits and utility rebate programs are spurring this burgeoning market. However, the reliability of these systems is virtually unknown. Recent work by Sandia National Laboratories (SNL) has shown that few data exist to quantify the mean time to failure of these systems. However, there is keen interest in developing new techniques to measure SHW reliability, particularly among utilities that use ratepayer money to pay the rebates. This document reports on an effort to develop and test new, simplified techniques to directly measure the state of health of fielded SHW systems. One approach was developed by the National Renewable Energy Laboratory (NREL) and is based on the idea that the performance of the solar storage tank can reliably indicate the operational status of the SHW systems. Another approach, developed by the University of New Mexico (UNM), uses adaptive resonance theory, a type of neural network, to detect and predict failures. This method uses the same sensors that are normally used to control the SHW system. The NREL method uses two additional temperature sensors on the solar tank. The theories, development, application, and testing of both methods are described in the report. Testing was performed on the SHW Reliability Testbed at UNM, a highly instrumented SHW system developed jointly by SNL and UNM. The two methods were tested against a number of simulated failures. The results show that both methods show promise for inclusion in conventional SHW controllers, giving them advanced capability in detecting and predicting component failures.

14 SOLAR ENERGY↗
DOE OSTI2015

Convergence of Vehicle and Infrastructure Data for Traffic and Demand Management

The increasing availability of highly granular, vehicle trajectory data combined with ever increasing stores of roadway sensor data has provided unparalleled observability into the operation of our urban roadway networks. These data sources are quickly moving from research and prototype environments into full-scale commercial deployment and data offerings. The observability gained allows for increased control opportunities to enhance transportation mobility, safety and energy efficiency. The National Renewable Energy Laboratory (NREL) is involved in three initiatives to leverage these data for positive outcomes: 1) In 2015 NREL, in cooperation with industry and university partners, was awarded an ARPA-E research grant to research a control architecture to incentivize individual travelers toward more sustainable travel behavior. Based on real-time data on the traveler's destination and state of the system, the traveler is presented with route and/or mode choices and offered incentives to accept sustainable alternatives over less-sustainable ones. The project tests the extent to which small incentives can influence, or tip the balance toward more sustainable travel behavior. 2) Although commercial sources of travel time and speed have emerged in recent years based on vehicle probe data, volume estimates continue to rely primarily on historical count data factored for the time of day, day of week, and season of year. Real-time volume flows would enable better tools, simulation in the loop, and ultimately more effective control outcomes. NREL in cooperation with the University of Maryland and industry traffic data providers (INRIX, HERE and TomTom), are attempting to accelerate the timeframe to a viable real-time vehicle volume data feed based on probe data. 3) Signal control on urban arterials for years has had to rely on models rather than measured data to assess performance. High-resolution controller data and low-cost re-identification data now allows for direct measurement of the quality of progression along a corridor. Though still requiring an investment in equipment and communications, these data sources are transforming traffic signal management to a data driven, performance management basis. Ever increasing availability of granular GPS trace data from automobiles may allow for assessment of traffic signal performance, allowing for signal optimization while minimizing the investment in additional sensors and communication infrastructure.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Measurement-informed Dynamic Aggregation of Distribution Systems

This paper proposes a measurement-informed dynamic aggregation methodology in order to create equivalent representations of distribution systems that are compatible with large-scale transmission analysis. By optimizing an equivalent feeder parameters using time-series measurements of active power, reactive power, and voltage at the Point of Interconnection (POI), the approach yields simplified yet dynamically accurate equivalents. Implemented in PSCAD with models of photovoltaic–battery systems, three-phase motors, and static loads, the method employs hybrid differential evolution and bounded least-squares optimization laying the foundation for for real-time state estimation and optimized sensor placement in distribution networks.

Ahmed, Kazi Ishrak [University of Tennessee, Knoxv↗
DOE OSTI2010

Performance characteristics of a perforated shadow band under clear sky conditions

A perforated, non-rotating shadow band is described for separating global solar irradiance into its diffuse and direct normal components using a single pyranometer. Whereas shadow bands are normally solid so as to occult the sensor of a pyranometer throughout the day, the proposed band has apertures cut from its circumference to intermittently expose the instrument sensor at preset intervals. Under clear sky conditions the device produces a saw tooth waveform of irradiance data from which it is possible to reconstruct separate global and diffuse curves. The direct normal irradiance may then be calculated giving a complete breakdown of the irradiance curves without need of a second instrument or rotating shadow band. This paper describes the principle of operation of the band and gives a mathematical model of its shading mask based on the results of an optical ray tracing study. An algorithm for processing the data from the perforated band system is described and evaluated. In an extended trial conducted at NREL's Solar Radiation Research Laboratory, the band coupled with a thermally corrected Eppley PSP produced independent curves for diffuse, global and direct normal irradiance with low mean bias errors of 5.6 W/m{sup 2}, 0.3 W/m{sup 2} and -2.6 W/m{sup 2} respectively, relative to collocated reference instruments. Random uncertainties were 9.7 W/m{sup 2} (diffuse), 17.3 W/m{sup 2} (global) and 19.0 W/m{sup 2} (direct). When the data processing algorithm was modified to include the ray trace model of sensor exposure, uncertainties increased only marginally, confirming the effectiveness of the model. Deployment of the perforated band system can potentially increase the accuracy of data from ground stations in predominantly sunny areas where instrumentation is limited to a single pyranometer. (author)

14 SOLAR ENERGY↗
DOE OSTI2016

Development of a MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the NSRDB

A significant source of bias in the transposition of global horizontal irradiance to plane-of-array (POA) irradiance arises from inaccurate estimations of surface albedo. The current physics-based model used to produce the National Solar Radiation Database (NSRDB) relies on model estimations of surface albedo from a reanalysis climatalogy produced at relatively coarse spatial resolution compared to that of the NSRDB. As an input to spectral decomposition and transposition models, more accurate surface albedo data from remotely sensed imagery at finer spatial resolutions would improve accuracy in the final product. The National Renewable Energy Laboratory (NREL) developed an improved white-sky (bi-hemispherical reflectance) broadband (0.3-5.0 ..mu..m) surface albedo data set for processing the NSRDB from two existing data sets: a gap-filled albedo product and a daily snow cover product. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites have provided high-quality measurements of surface albedo at 30 arc-second spatial resolution and 8-day temporal resolution since 2001. The high spatial and temporal resolutions and the temporal coverage of the MODIS sensor will allow for improved modeling of POA irradiance in the NSRDB. However, cloud and snow cover interfere with MODIS observations of ground surface albedo, and thus they require post-processing. The MODIS production team applied a gap-filling methodology to interpolate observations obscured by clouds or ephemeral snow. This approach filled pixels with ephemeral snow cover because the 8-day temporal resolution is too coarse to accurately capture the variability of snow cover and its impact on albedo estimates. However, for this project, accurate representation of daily snow cover change is important in producing the NSRDB. Therefore, NREL also used the Integrated Multisensor Snow and Ice Mapping System data set, which provides daily snow cover observations of the Northern Hemisphere for the temporal extent of the NSRDB (1998-2015). We provide a review of validation studies conducted on these two products and describe the methodology developed by NREL to remap the data products to the NSRDB grid and integrate them into a seamless daily data set.

14 SOLAR ENERGY↗
DOE OSTI2024

MODAQ (Modular Offshore Data Acquisition System) [SWR-24-58]

The National Renewable Energy Laboratory (NREL) has developed a data acquisition system to support laboratory (component) and in-water validation and testing of Marine Renewable Energy (MRE) technologies. The Modular Ocean Data AcQuisition System (MODAQ) is a robust system built on industrial grade hardware that can acquire high-quality and summary data from many sensor and instrument types. MODAQ includes modules to perform real-time QC screening, system health monitoring, and send alerts. MODAQ is also part of a larger data management, processing, visualization, and archiving system.

Raye, Robert↗
DOE OSTI2012

Appendix I3-1 to Wind HUI Initiative 1: AWST-WindNET-Phase 1 Final Report

This report is an appendix to the Hawaii WindHUI efforts to develop and operationalize short-term wind forecasting and wind ramp event forecasting capabilities. The report summarizes the WindNET Phase 1 efforts on the Big Island of Hawaii and includes descriptions of modeling methodologies, use of field validation data, results and recommendations. The objective of the WindNET project was to investigate the improvement that could be obtained in short-term wind power forecasting for wind generation facilities operating on the island grids operated by Hawaiian Electric Companies through the use of atmospheric sensors deployed at targeted locations. WindNET is envisioned as a multiphase project that will address the short-term wind forecasting issues of all of the wind generation facilities on the all of the Hawaiian Electric Companies' island grid systems. The first phase of the WindNET effort (referred to as WindNET-1) was focused on the wind generation facilities on the Big Island of Hawaii. With complex terrain and marine environment, emphasis was on improving the 0 to 6 hour forecasts of wind power ramps and periods of wind variability, with a particular interest in the intra-hour (0-1 hour) look-ahead period. The WindNET project was built upon a foundation that was constructed with the results from a previously completed observation targeting study for the Big Island that was conducted as part of a project supported by the National Renewable Energy Laboratory (NREL) and interactions with the western utilities. The observational targeting study provided guidance on which variables to measure and at what locations to get the most improvement in forecast performance at a target forecast site. The recommendations of the observation targeting study were based on the application two techniques: (1) an objective method called ensemble sensitivity analysis (ESA) (Ancell and Hakim, 2007; Torn and Hakim, 2008; Zack et al, 2010); and (2) a subjective method based on a diagnostic analysis of large ramp events. The analysis was completed for both the wind farm on the southern tip of the Big Island and on the northern tip of the island. The WindNET project was designed to also deploy sensors to validate the Big Island observational targeting study and enhance operator's understanding of predominate causes of wind variability conditions at the wind facilities. Compromises had to be made with the results from the observation targeting study to accommodate project resource limitations, availability of suitable sites, and other factors. To focus efforts, field sensor deployment activities focused on the wind facility on the southern point of Big Island.

14 SOLAR ENERGY↗
DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗
DOE OSTI2025

Standard Operating Procedure for Optimal Deployment of Meteorological Instrumentation Within the Solar Radiation Research Laboratory: 2025 Edition

The objective of the National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) is to collect and use high-quality solar radiation data sets for research leading to the widespread adoption of solar technologies. To appropriately populate and track the diverse array of instruments at the NREL-SRRL, NREL has established a Standard Operating Procedure (SOP) for optimal instrument deployment within the SRRL for both the Baseline Measurement System (BMS) and the Research Measurement System (RMS). Using best practices methodologies, the NREL-SRRL maintains a varied and extensive array of solar monitoring equipment to test, evaluate, and characterize the solar sensors used by federal and international agencies as well as the solar industry to determine the solar resource. The SOP provides the industry with guidance for solar resource assessment and is used for procedures in the long-term continuous monitoring of legacy instruments alongside state-of-the-art instruments. Based on the SOP, instruments are annually evaluated for continued deployment. Instruments that do not meet the SOP criteria are decommissioned, and new instruments that meet the criteria are deployed. Streamlining and optimizing the use of this facility ensures that the lab continues to be a world-leading solar calibration and measurement facility. This 2025 edition includes updates to the appendices to describe the current instrumentation of the NREL-SRRL.

14 SOLAR ENERGY↗
DOE OSTI2012

Effect of Manufacturing-Induced Defects on Reliability of Composite Wind Turbine Blades

In support of DOE's efforts on developing affordable, reliable domestic wind power, this ARRA project brought together a strong, complementary team from academia (University of Massachusetts Lowell), two DOE laboratories (NREL and Sandia), and a major wind turbine blade manufacturer (TPI) to address one of the key issues affecting wind power cost and reliability manufacturing-induced defects in the blades. The complexity of this problem required the assembled team's expertise in materials specifically textile and composite structures finite element modeling, composites manufacturing, mechanical characterization, structural dynamics, nondestructive inspection (NDI) and structural health monitoring (SHM), sensors, and wind turbine blade testing. This final report summarizes the results of this project.

17 WIND ENERGY↗
DOE OSTI2026

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗
DOE OSTI2018

Laboratory Resources and Techniques to Evaluate Smart Home Technology: Preprint

Smart home technologies promise to make residential buildings more comfortable, less costly to operate, more energy efficient, and more integrated with the grid, but these systems are difficult to evaluate in traditional laboratory spaces. To aid in the development and evaluation of new home energy management systems (HEMS), NREL built the Systems Performance Laboratory (SPL) within the Energy Systems Integration Facility. SPL has electrical infrastructure for three residential buildings, complete with smart meters, breaker panels and a full complement of major and minor appliances in each 'home.' Cord and plug power connections mean that appliances can easily be swapped for different models. Distributed energy resources (DER), such as residential photovoltaic (PV) inverters and home batteries, are also available for integration. Sensors monitor the power consumption of all loads and controllers can simulate occupancy to drive the appliances in realistic ways. A novel hardware-in-the-loop application was developed to ensure air conditioner cycling by combining a building simulation with a physical air conditioner and a thermostat, which allows us to simulate performance in any climate with any building construction. Another application of the hardware-in-the-loop technique uses a grid simulation to model the grid impact for different control strategies related to smart home technologies. This laboratory is a powerful, flexible tool that can be employed to evaluate the effect that home energy management and other smart home technologies will have at the appliance, whole-home, and distribution feeder levels - all of which are important perspectives for different stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

OPET Hardware (Open PV Electrical Tool Hardware) [SWR-25-42]

OPET (Open-source Photovoltaic Electrical Tool) is used for performance measurements of solar photovoltaic (PV) devices in the field under natural sunlight or in the lab under artificial light. Its primary use is in research and development of solar cells and modules, specifically in reliability and durability research of PV devices. Some features and functions include: -IV curve measurements with linear or cosine distributed measurement points -PV device active loading at open circuit voltage (Voc), short circuit current (Isc) and maximum power point (Pmp) -Bias power supply to overcome series resistance in contact wires for Isc measurements and loading -PV voltage input in five ranges from 1V to 100V -PV current input ranges -Low current version, six current ranges from 1.1mA to 340mA -High current version, six current ranges from 50mA to 15A -IO ports for I2C and SPI temperature sensor Arduino extension boards -Integrated fan control This repository contains everything relating to the hardware of the OPET device. If you are looking for the firmware or software repositories, links are below: https://github.com/NREL/opet-firmware https://github.com/NREL/opet-control

McDanold, Byron [National Renewable Energy Laborat↗
DOE OSTI2016

A Synchronized Sensor Array for Remote Monitoring of Avian and Bat Interactions with Offshore Renewable Energy Facilities

Wind energy production in the U.S. is projected to increase to 35% of our nation’s energy by 2050. This substantial increase in the U.S. is only a portion of the global wind industry growth, as many countries strive to reduce greenhouse gas emissions. A major environmental concern and potential market barrier for expansion of wind energy is bird and bat mortality from impacts with turbine blades, towers, and nacelles. Carcass surveys are the standard protocol for quantifying mortality at onshore sites. This method is imperfect, however, due to survey frequency at remote sites, removal of carcasses by scavengers between surveys, searcher efficiency, and other biases as well as delays of days to weeks or more in obtaining information on collision events. Furthermore, carcass surveys are not feasible at offshore wind energy sites. Near-real-time detection and quantification of interaction rates is possible at both onshore and offshore wind facilities using an onboard, integrated sensor package with data transmitted to central processing centers. We developed and experimentally tested an array of sensors that continuously monitors for interactions (including impacts) of birds and bats with wind turbines. The synchronized array includes three sensor nodes: (1) vibration (accelerometers and contact microphones), (2) optical (visual and infrared spectrum cameras), and (3) bioacoustics (acoustic and ultrasonic microphones). Accelerometers and contact acoustic microphones are placed at the root of each blade to detect impact vibrations and sound waves propagating through the structure. On-board data processing algorithms using wavelet analysis detect impact signals exceeding background vibration. Stereo-visual and infrared cameras were placed on the nacelle to allow target tracking, distance, and size calculations. On-board image processing and target detection algorithms identify moving targets within the camera field of view. Bioacoustic recorders monitor vocalizations and echolocations to aid in identifying organisms involved in interactions. Data from all sensors are temporarily stored in ring (i.e., circular) buffers with a duration varying by sensor type. Detection of target presence or impact by any of the sensors can trigger the archiving of data from all buffers for transmission to a central data processing center for evaluation and post-processing. This mitigates the risk of “data mortgages” posed by continual recording and minimizes personnel time required to manually review event data. We first conducted individual component tests at laboratories and field sites in Corvallis and Newport, Oregon, and Seattle and Sequim, Washington. We conducted additional component tests on research wind turbines at the North American Wind Research and Training Center, Mesalands Community College (MCC; General Electric 1.5 MW turbine), New Mexico, and the National Wind Technology Center, National Renewable Energy Laboratory (NREL; Controls Advanced Research Turbines 3 [CART 3] 600 kW Westinghouse turbine), Colorado. We conducted fully integrated system tests at NREL in October 2014 and April 2015. We used only research wind turbines so that we could conduct controlled, experimentally generated impacts using empty and water-filled tennis balls shot from a compressed air cannon on the ground. The approx. 57-140 g tennis balls (depending on water content) were at the upper mass range for bats, but lower mass range for marine birds. Therefore, the ability to detect collisions of most seabirds is likely greater than our experiments demonstrate, but possibly lower for some bats depending on the background signal of a given turbine. Vibration data demonstrated that background signals of operating turbines varied markedly among the CART 3 under normal operation (greatest), GE (moderate), and CART 3 during idle rotation (generator not engaged; least). In total, we measured 63 experimental blade impacts on the two research turbines. Impaction detection was dependent on background signals, position of impact on the blade (a tip strike resulted in the strongest impact signal), and impact kinetics (velocity of ball and whether the ball struck the surface of the blade or the leading edge of the blade struck the ball). Overall detection percentage ranged from 100% for the “quietest” conditions (CART 3 idle rotation), down to 35% for the noisiest (CART 3 normal operation). Impact signals were detected from sensors on more than one blade (i.e., blades other than the blade struck) 50% - 75% of the time. Stereo imaging provided valuable metrics, but increased data processing and equipment cost. Given the cost of cameras with sufficient resolution for target identification, we suggest mounting cameras directly on the blades to continuously view the entire rotor swept area with the fewest number of cameras. Bioacoustic microphones provide taxonomic identification, as well as information on ambient noise levels. They also assist in identifying environmental conditions such as hail storms, high winds, thunder, lightning, etc., that may contribute to a collision or a false positive detection. We demonstrated a proof of concept for an integrated sensor array to detect and identify bird and bat collisions with wind turbines. The next phase of research and development for this system will miniaturize and integrate sensors from all three nodes into a single wireless package that can be attached directly to the blade. This next generation system would use all “smart” sensors capable of onboard data processing to drastically reduce data streams and processing time on a central computer. A provisional patent for the blade mounted system was submitted by Oregon State University and recorded by the U.S. Patent and Trademark Office (application no. 62313028). Eventually, technology and industry advances will allow this low cost monitoring system to be designed into materials during manufacturing so that all turbines could be monitored with either a subset or full suite of sensors. As standard equipment on all commercial turbines, the sensor suite would allow the industry to effectively monitor whether individual turbines were causing mortalities or not and under what circumstances. It would also provide real-time evaluation of mechanical and structural integrity of a turbine via vibration, image, and acoustic data streams, thereby permitting modifications in operation to limit environmental or mechanical damage.

17 WIND ENERGY↗
DOE OSTI2026

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

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

Sandhu, Rimple [National Laboratory of the Rockies↗