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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Collaborative Research: Understanding air‐sea feedbacks to the MJO through process evaluation of observations and E3SM experiments (Final Technical Report)

This project explored the role of tropical oceans in regulating cloud populations within the Madden-Julian oscillation (MJO)–a large-scale tropical weather disturbance that influences extreme weather events across the globe by affecting the position of the jet stream and patterns of moisture transport. Our project sought to 1) document ocean feedback strength to the MJO in climate models, 2) understand how air-sea interactions affect the intensity, organization, and propagation of MJO convection, 3) utilize existing observations to identify sources of model bias for tropical air-sea interactions, and 4) better understand the effects of slow and fast ocean variability on MJO cloudiness. We found that transfers of heat and moisture across the air-sea interface (i.e., surface fluxes) in most climate models are over-supportive in maintaining MJO convection and under-supportive in propagating MJO convection. This can lead to misrepresentations in the frequency of extreme weather events across the globe through erroneous MJO feedbacks to jet stream position and moisture transport. Furthermore, using observations collected by ocean moorings across the tropical oceans, we showed that most models greatly overestimate surface evaporation in the subtropics, and that this bias is likely one contributor to a pervasive bias among climate models in their ability to simulate the mean state of rainfall over the tropical oceans (i.e., the “double ITCZ bias”). Replacing the default surface flux algorithm in two climate models with a state-of-the-art surface flux algorithm modestly reduced the double ITCZ bias in those models. Our work produced two model diagnostics packages that are now publicly available through DOE and NOAA model diagnostics archives. The first, the Analysis of Scales of Precipitation (ASoP) diagnoses scales of precipitation variability across space and time, with temporal resolution as fine as the model integration timestep. The second, the air-sea flux diagnostic diagnoses model surface fluxes and allows direct comparison of model fluxes to observations without having to recompute fluxes using model output and a state-of-the-art flux algorithm. These diagnostics, which were developed using climate models, can be readily applied to regional and global forecast model output to improve forecasts for the benefit of society.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning deciphers CO 2 sequestration and subsurface flowpaths from stream chemistry

Endmember mixing analysis (EMMA) is often used by hydrogeochemists to interpret the sources of stream solutes, but variations in stream concentrations and discharges remain difficult to explain. We discovered that machine learning can be used to highlight patterns in stream chemistry that reveal information about sources of solutes and subsurface groundwater flowpaths. The investigation has implications, in turn, for the balance of CO 2 in the atmosphere. For example, CO 2 -driven weathering of silicate minerals removes carbon from the atmosphere over ~106-year timescales. Weathering of another common mineral, pyrite, releases sulfuric acid that in turn causes dissolution of carbonates. In that process, however, CO 2 is released instead of sequestered from the atmosphere. Thus, understanding long-term global CO 2 sequestration by weathering requires quantification of CO 2 - versus H 2 SO 4 -driven reactions. Most researchers estimate such weathering fluxes from stream chemistry, but interpreting the reactant minerals and acids dissolved in streams has been fraught with difficulty. We apply a machine-learning technique to EMMA in three watersheds to determine the extent of mineral dissolution by each acid, without pre-defining the endmembers. The results show that the watersheds continuously or intermittently sequester CO 2 , but the extent of CO 2 drawdown is diminished in areas heavily affected by acid rain. Prior to applying the new algorithm, CO 2 drawdown was overestimated. The new technique, which elucidates the importance of different subsurface flowpaths and long-timescale changes in the watersheds, should have utility as a new EMMA for investigating water resources worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Arctic cyclones have become more intense and longer-lived over the past seven decades

Intense cyclones driving extreme Arctic weather and climate events have been more frequently observed during recent years, causing dramatic environmental and socioeconomic impacts. However, inconsistencies have emerged about long-term changes in Arctic cyclone activity. Here we analyze multiple reanalysis datasets covering a multidecadal period with improvements to the cyclone tracking algorithm and the integrated cyclone activity metric. The results indicate an intensification of Arctic cyclone activity over the last seven decades. There has been a long-term shift of the maximum cyclone counts from weaker to stronger cyclones and a pronounced lengthening of the duration of strong cyclones. Spatial analysis shows increased strong cyclone frequency over the Arctic, driven by enhanced lower troposphere baroclinicity, amplified winter jet stream waves over the subpolar North Atlantic, and a strengthened summer tropospheric vortex over the central Arctic. The stratospheric vortex has also intensified the tropospheric waves and vortex with distinct dynamics between winter and summer. Recently enhanced baroclinicity over large areas of the Arctic and midlatitudes suggests more complicated atmospheric dynamics than what is hypothesized with Arctic-amplification-induced decrease in meridional temperature gradients.

Atmospheric dynamics↗
DOE OSTI2023

SPLENDAQ: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments

Many scientific applications from rare-event searches to condensed matter system characterization to high-rate nuclear experiments require time-domain triggering on a raw stream of data, where the triggering is generally threshold-based or randomly acquired. When carrying out detector R &D, there is a need for a general data acquisition (DAQ) system to quickly and efficiently process such data. In the SPLENDOR collaboration, we are developing the Python-based SPLENDAQ package for this exact purpose—it offers two main features for offline analysis of continuous data: a threshold triggering algorithm based on the time-domain optimal filter formalism and an algorithm for randomly choosing nonoverlapping segments for noise measurements. Further, combined with the commercially available Moku platform, developed by Liquid Instruments, we have a full pipeline of event building off raw data with minimal setup. Here, we review the underlying principles of this detector-agnostic DAQ package and give concrete examples of its utility in various applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

Underutilized wet waste is a promising feedstock for production of carbon-neutral or carbon-negative liquid transportation fuels. Arrested methanogenesis of wet wastes by microbes, which produces mixtures of C2-C8 carboxylic acids (volatile fatty acids, VFAs), is practiced industrially at a pilot scale and the VFA products can be catalytically upgraded to molecules suitable for use as transportation fuels (alcohols and alkanes) via sequential ketonization and hydrogenation, both steps of which have been demonstrated with high (>90%) yield at the lab scale. We present in this work a simulation-based decision-making framework which evaluates upgrading strategies of VFAs to alcohol- and hydrocarbon-based neat and blended diesel, jet, and gasoline fuels. The processes utilize sequential upgrading steps (ketonization and hydrogenation) accompanied by one distillation step (either separating VFAs before ketonization or alcohols after ketone hydrogenation) to form two liquid streams: a light alcohol stream best suited as a light-duty fuel and a heavy alcohol or hydrocarbon stream best suited as a diesel or jet fuel. Suitability of the liquid products as transportation fuels and maximum blending levels with petrofuels comes from simulation of critical fuel properties (boiling point, flash point, lower heating value, viscosity, melting point, water solubility, and cetane/octane number). Catalytic upgrading steps and fuel property mixing models used by the simulation are experimentally validated. We demonstrate the flexibility of the decision-making algorithm by evaluating processing scenarios for experimentally-observed VFA profiles with varied carbon chain length distributions. Processing scenarios are optimized for either maximum total renewable carbon utilization or production of heavy-duty (jet or diesel) fuels. We evaluate the economic viability and CO2 emissions of each proposed upgrading scenario using techno-economic and life cycle analyses. The decision-making framework developed in this work can also be used to down-select promising strategies for upgrading of other bio-based feedstocks to sustainable fuels and chemicals.

aviation fuel↗
DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2026

The high level trigger and express data production at STAR

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

FPV Video Adaptation for UAV Collision Avoidance

First person view (FPV) technology for unmanned aerial vehicles (UAVs) provides an immersive experience for pilots and enables various personal and commercial applications such as aerial photography, drone racing, search and rescue operations, agricultural surveillance, and structural inspection. While real time video streaming from a UAV and vision-based collision avoidance strategies have been studied in literature as separate topics, in this paper we tackle collision avoidance in FPV scenarios, taking into account network delays and real time video parameters. We present a theoretical model for obstacle collisions that considers the current communication channel conditions, the real time video parameters, and the UAV's position relative to the closest obstacle. A video adaptation algorithm is then designed, using this metric, to tune the FPV video resolution, number of re-transmission attempts, and the modulation scheme to maximize the probability of avoiding collisions. This algorithm also takes into account specific latency constraints of the application. This video algorithm was evaluated in various scenarios and its ability to respond to both distances to the obstacle as well as the communication channel conditions was demonstrated. It was found that, for the considered scenarios, the performance of the proposed adaptive algorithm was, on an average, 58.63% higher than the closest non-adaptive one in terms of maximizing the probability of avoiding collision. Such collision avoidance strategies could be used to make UAV FPV applications safer and more reliable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗
DOE OSTI2022

The MP{sub N} method: a new angular discretization method based on piecewise polynomial interfaces fluxes

In transport calculations, it is well known how S{sub N} method is extremely inefficient in problems where the particle physics is dominated by streaming. The ray-effect eventually produced by the insufficient angular discretization, appears to be extremely persistent with respect to the refinement of the angular quadrature. The MP{sub N} method, that relies on continuous angular representation, offers a robust remedy to such an issue. MP{sub N} is based on the decomposition of the unit sphere into solid angles and on a piecewise continuous definition of interface fluxes, which are expanded in polynomials in each solid angle. This allows propagating more than one angular degree of freedom simultaneously while maintaining unaltered the block-diagonal pattern of the displacement plus removal operator. The method is therefore well suited for the flux resolution by means of a conventional sweep algorithm. Furthermore, unlike the S{sub N} method, MP{sub N} does not rely on discrete directions and, thus, on an angular quadrature formula, but rather constructs a set of linear equations solving for the angular moments of the flux for all discrete solid angles within the sweep. MP{sub N} shows an error convergence rate higher than S{sub N} at the expense of an increased size of the coefficient matrices, so of the computational cost. Although MP{sub N} is not free from ray-effect, the latter is effectively mitigated and less persistent with respect to the increase of the angular refinement order. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗
DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗
DOE OSTI2025

Modeling and Analysis of Clean Energy and Storage Technologies (CRADA Final Report, Project 1)

The goal of this project is to provide Southern Company Services, Inc. ("Participant") with custom scripts that can be used to create an average PV energy production profile, calculate lifetime energy value, calculate capacity value, and calculate the resultant financial metrics considering those value streams. Secondly, a fuel-cell model will be added to the public version of System Advisor Model (SAM). This standalone technology will incorporate PV and battery storage, allowing the participant to model the interaction of these three technologies. By adding this capability to a public version of SAM, a broad audience will be able to consider the system performance and financial benefits of installing a fuel cell as a baseline generator with PV. Thirdly, automated dispatch algorithms will be developed and added to SAM. These algorithms will enable Southern Company to dispatch a DC-connected front-of-the-meter battery system while considering price signals and PV clipping behavior. By adding these capabilities to a public version of SAM, users will be able to consider more complex and realistic ways of dispatching a battery system.

14 SOLAR ENERGY↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Heterogeneous data-processing optimization with CLARA’s adaptive workflow orchestrator

The hardware landscape used in HEP and NP is changing from homogeneous multi-core systems towards heterogeneous systems with many different computing units, each with their own characteristics. To achieve maximum performance with data processing, the main challenge is to place the right computing on the right hardware. In this paper, we discuss CLAS12 charge particle tracking workflow orchestration that allows us to utilize both CPU and GPU to improve the performance. The tracking application algorithm was decomposed into micro-services that are deployed on CPU and GPU processing units, where the best features of both are intelligently combined to achieve maximum performance. In this heterogeneous environment, CLARA aims to match the requirements of each micro-service to the strength of a CPU or a GPU architecture. A predefined execution of a micro-service on a CPU or a GPU may not be the most optimal solution due to the streaming data-quantum size and the data-quantum transfer latency between CPU and GPU. So, the CLARA workflow orchestrator is designed to dynamically assign micro-service execution to a CPU or a GPU, based on the online benchmark results analyzed for a period of real-time data-processing.

Gyurjyan, Vardan↗
DOE OSTI2020

A High-Throughput Solver for Marginalized Graph Kernels on GPU

Here, we present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs. To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory. This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. We propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to further exploit sparsity. We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales.

97 MATHEMATICS AND COMPUTING↗
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↗