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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Dual-Element Tunneling Accelerometer

Improved micromachined tunneling accelerometer contains two deflecting transducer elements: One an elastically supported proof mass having relatively low resonant frequency; other cantilever tunneling transducer that tracks displacement of proof mass and has relatively high resonant frequency ({sup a} 10 kHz). Deflection voltage generated by circuit like described in "Wideband Feedback Circuit for Tunneling Sensor" (NPO-18866). Accelerometers of this type suited for underwater acoustic measurements, detecting vibrations associated with malfunctions in vehicles, detecting seismic signals, monitoring and controlling vibrations in structures, and other applications.

Kaiser, William J.↗
DOE OSTI2020

Nuclear Interdiction Through Relocatable Detectors

The NNSA Office of Nuclear Smuggling Detection and Deterrence (NSDD) has investigated a set of minimal-infrastructure radiation detection systems as alternatives to fixed Radiation Portal Monitors (RPMs) for nuclear interdiction applications. These versatile and relocatable systems can improve nuclear security in missions or locations that do not warrant or support a standard fixed radiation detection system. Over 2019, a variety of relocatable detectors were characterized at the Interdiction Technologies Integration Laboratory at Pacific Northwest National Laboratory (PNNL). Evaluated detectors were diverse in their size and capabilities, ranging from backpack-sized systems to lane-spanning cargo scanning portals. Both spectroscopic and non-spectroscopic pedestrian and vehicle detection systems were characterized against uranium and plutonium sources. Signatures from the sources were modulated by both shielding and distance to quantify the performance of the relocatable systems as signal strength was decreased. Findings showed that relocatable spectroscopic detectors with isotope identification capabilities could reduce nuisance alarm rates compared to conventional fixed installation, gross-counting, radiation portal monitors. In vehicle scanning applications, detection ability generally trended with detection volume, regardless of spectral capability. In pedestrian scanning applications, several smaller backpack-sized detector systems were found to be more sensitive to detecting material than pedestrian portal monitors. The results of this characterization effort have helped inform the deployment of versatile equipment to improve nuclear security missions.

relocatable detectors, Nuclear Security↗
NASA NTRS1980

A sensor for control of arterials and networks

The sensor for control of arterials and networks (SCAN) uses imaging technology and processing technology to jointly provide real-time quantification. The SCAN concept consists of a television camera mounted on a pole to obtain images of the traffic, and a microprocessor to process the image data into traffic parameters. The current activities focus on the development of SCAN for surveillance of arterials or urban highways. If these efforts are successful, an attempt will be made to extend the concept to network applications. The key software developed for the SCAN is a vehicle detection and tracking algorithm which reduces the TV image data to vehicle descriptions and trajectories. These preprocessed trajectory data can be transmitted over a phone line or can be easily reduced to a wide range of traffic parameters. A SCAN breadboard has been implemented and installed in a van which enables remote field tests and evaluations. The SCAN breadboard and its operation, evaluation, and potential applications are described.

Hilbert, E. E.↗
NASA NTRS2004

System Would Predictively Preempt Traffic Lights for Emergency Vehicles

Two electronic communication-and-control systems have been proposed as means of modifying the switching of traffic lights to give priority to emergency vehicles. Both systems would utilize the inductive loops already installed in the streets of many municipalities to detect vehicles for timing the switching of traffic lights. The proposed systems could be used alone or to augment other automated emergency traffic-light preemption systems that are already present in some municipalities, including systems that recognize flashing lights or siren sounds or that utilize information on the positions of emergency vehicles derived from the Global Positioning System (GPS). Systems that detect flashing lights and siren sounds are limited in range, cannot "see" or "hear" well around corners, and are highly vulnerable to noise. GPS-based systems are effective in rural areas and small cities, but are often ineffective in large cities because of frequent occultation of GPS satellite signals by large structures. In contrast, the proposed traffic-loop forward prediction system would be relatively invulnerable to noise, would not be subject to significant range limitations, and would function well in large cities -- even in such places as underneath bridges and in tunnels, where GPS-based systems do not work. One proposed system has been characterized as "car-active" because each participating emergency vehicle would be equipped with a computer and a radio transceiver that would communicate with stationary transceivers at the traffic loops. The other proposed system has been characterized as "car-passive" because a passive radio transponder would be installed on the underside of a participating vehicle.

Bachelder, Aaron↗
DOE OSTI2021

Description of SIS-AOP Result Format V1.0

Single Image SICD-Based Automatic Object Processing (SIS-AOP) is an automatic object identification tool for SAR imagery. It ingests a SAR image in standard SICD format, and it will run a suite of algorithms to cue possible vehicle detections, cull those detections and then ultimately label them either as detections only or possible expound to give a class-level ID or a vehicle-type ID. The SIS-AOP results are given in an XML (Extensible Markup Language) output format. This document defines the elements in the SISAOPR XML output format.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Estimating vehicle fuel economy from overhead camera imagery and application for traffic control

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Dataset (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the dataset utility through three applications: the estimate of fuel consumption based on segmentation bounding boxes, vehicle discrimination for those vehicles with largest bounding boxes, and a fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set based on web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results show the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras.

Karnowski, Thomas↗
NASA NTRS1993

Preliminary analysis of the JAPE ground vehicle test data with an artificial neural network classifier

Remotely sensing and classifying military vehicles in a battlefield environment have been the source of much research over the past 20 years. The ability to know where threat vehicles are located is an obvious advantage to military personnel. In the past active methods of ground vehicle detection such as radar have been used, but with the advancement of technology to locate these active sensors, passive sensors are preferred. Passive sensors detect acoustic emissions, seismic movement, electromagnetic radiation, etc., produced by the target and use this information to describe it. Deriving the mathematical models to classify vehicles in this manner has been, and is, quite complex and not always reliable. However, with the resurgence of artificial neural network (ANN) research in the past few years, developing models for this work may be a thing of the past. Preliminary results from an ANN analysis to the tank signatures recorded at the Joint Acoustic Propagation Experiment (JAPE) at the US Army White Sands Missile Range, NM, in July 1991, are presented.

Larsen, Nathan F.↗
DOE OSTI2026

Firmware Tampering Detection in Heavy-Duty Vehicles through J1939 CAN Analysis

Modern heavy-duty vehicles rely on complex networks of Electronic Control Units (ECUs) that communicate using the J1939 protocol. While this system makes it easier to update and configure vehicle components, it also opens the door to serious cybersecurity risks if not properly secured. This work investigates the potential for firmware tampering through the J1939 communication protocol, which enables ECU configuration and reprogramming over the Controller Area Network (CAN) bus. By monitoring CAN traffic during legitimate maintenance operations and reverse-engineering OEM diagnostic software, we identified common and proprietary J1939 message identifiers, authentication patterns, and vulnerabilities within Unified Diagnostic Services (UDS). These findings demonstrate that inadequate authentication mechanisms can allow malicious actors to alter ECU firmware or disable safety functions, posing severe operational and safety risks. Our analysis contributes to the development of vehicle intrusion detection systems capable of recognizing abnormal reprogramming activity and future firmware fingerprinting methods to verify software integrity across ECUs. This work highlights the importance of standardizing secure firmware authentication across manufacturers to strengthen cyber resilience in heavy-duty vehicle systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
NASA NTRS1993

Joint Acoustic Propagation Experiment (JAPE)

The Joint Acoustic Propagation Experiment (JAPE), performed under the auspices of NATO and the Acoustics Working Group, was conducted at White Sands Missile Range, New Mexico, USA, during the period 11-28 Jul. 1991. JAPE consisted of 220 trials using various acoustic sources including speakers, propane cannon, various types of military vehicles, helicopters, a 155mm howitzer, and static high explosives. Of primary importance to the performance of these tests was the intensive characterization of the atmosphere before and during the trials. Because of the wide range of interests on the part of the participants, JAPE was organized in such a manner to provide a broad cross section of test configurations. These included short and long range propagation from fixed and moving vehicles, terrain masking, and vehicle detection. A number of independent trials were also performed by individual participating agencies using the assets available during JAPE. These tests, while not documented in this report, provided substantial and important data to those groups. Perhaps the most significant feature of JAPE is the establishment of a permanent data base which can be used by not only the participants but by others interested in acoustics. A follow-on test was performed by NASA LaRC during the period 19-29 Aug. 1991 at the same location. These trials consisted of 59 overflights of supersonic aircraft in order to establish the relationship between atmospheric turbulence and the received sonic boom energy at the surface.

Carnes, Benny L.↗
NASA NTRS1992

Traffic Monitor

Mestech's X-15 "Eye in the Sky," a traffic monitoring system, incorporates NASA imaging and robotic vision technology. A camera or "sensor box" is mounted in a housing. The sensor detects vehicles approaching an intersection and sends the information to a computer, which controls the traffic light according to the traffic rate. Jet Propulsion Laboratory technical support packages aided in the company's development of the system. The X-15's "smart highway" can also be used to count vehicles on a highway and compute the number in each lane and their speeds, important information for freeway control engineers. Additional applications are in airport and railroad operations. The system is intended to replace loop-type traffic detectors.

Source record↗
NASA NTRS2012

NASA Tech Briefs, April 2012

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

Source record↗
NASA NTRSDate not supplied

Geometric Assessment of PlanetScope Imagery

Commercial companies such as Maxar, Planet Labs, and BlackSky have built large archives of high resolution (1-3 m) Earth observing data. The high temporal resolution and global coverage of these satellites makes commercial satellite images ideal for scientists studying rapidly changing processes such as flooding and fires. The high spatial resolution allows scientists to push studies to a fine scale such as monitoring agricultural growth experiments or detecting vehicles for traffic management. Scientists using these data need to know geolocation accuracy, band-to-band registration (BBR), and pixel footprint size to best determine appropriate applications as well as what extra steps they will need to take to use the data. Here, we find those key geometric properties for images from Planet Labs’ SuperDove constellation. Geolocation accuracy is assessed relative to WorldView images at globally distributed cities, band-to-band registration (BBR) is examined for every image we acquired (200+ images), and pixel footprint sizes are calculated from edge responses of the images over Cal/Val sites in China, India, or USA both near launch time and 1+ years after launch. We find that SuperDove geolocation is highly self-consistent with average global offsets among SuperDove images of 3.80 m (1.27 pixels). Despite this self-consistency, relative geolocation accuracy (with WorldView images as reference) varies by location from 3.20 m – 28.09 m mean offset. SuperDove BBR is sub-pixel and varies by band, with a mean radial offset relative to red band varying from 0.39 - 1.13 m. SuperDove image footprint size improves after 1+ years in orbit, with an average footprint size of 3.41 pixels (10.24 m) soon after launch and 3.23 pixels (9.7 m) 1+ years after launch. The BBR and self-consistency offsets are relatively small, given the large footprint sizes. A similarly extensive investigation is in progress for Planet Labs’ Dove-R series.

Alana G. Semple↗
DOE OSTI2024

Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network

This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗