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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bringing Different Views Together: A Hybrid Cooperative Perception Framework for Connected Autonomous Vehicles

Cooperative perception will be essential for connected autonomous vehicles to enhance object recognition and optimize path planning by sending data information about the surrounding environment. However, an inherent challenge in existing systems is the high bandwidth cost of transmitting information in real-time, which restricts cooperative perception’s practicality. Here, this work presents a hybrid cooperative perception fusion framework aimed at mitigating this issue by optimizing data transmission according to available bandwidth or through data reduction techniques. Our methods ensure that vehicles can rapidly transmit high-confidence data without overwhelming the network. Experimental results indicate that our methodology substantially diminishes data transmission sizes while maintaining object detection accuracy. For cooperative perception in autonomous vehicle systems, our approach provides a scalable and effective way to get past the bandwidth barrier.

Carrillo, Dominic [Univ. of North Texas, Denton, T↗
DOE OSTI2024

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

Recent advancement in autonomous driving vehicles and V2X communication has attracted increasing attention towards Intelligent Transportation Systems to build a safe and reliable traffic intersection. However, most of the systems are still at the initial stages and require significant progress to become a reality. This paper presents an overview of NREL Infrastructure Perception and Control (IPC) framework which is an open-source track-data fusion engine which takes input from infrastructure-based perception sensors and cooperatively shared messages from Connected Autonomous Vehicles (CAVs) and Connected Vehicle (CVs) and the challenges associated with deploying such cooperative perception framework at a four-way traffic intersection in the city of Colorado Springs, CO, USA. The sensor data is collected by deploying two radars and two LiDAR sensors on the IPC mobile lab and two radars on diagonally opposite traffic poles at the proposed intersection. The sensor output results imply the need for rapid sensor calibration to bring the collective perception to a common coordinate frame, the importance of time synchronization between the sensors in order to capture accurate spatial and temporal alignment of the objects, and the need for a health monitoring system with fail safe closed-loop detection model for real-time deployment.

camera↗
DOE OSTI2022

Concept of Operations of Next-Generation Traffic Control Utilizing Infrastructure-Based Cooperative Perception

This paper provides a system architecture for an infrastructure-based cooperative perception fusion engine for next-generation traffic control. This engine will provide a complete state-space digital representation with measurable accuracy to support a wide-range of applications. The architecture includes inputs, functional flow, data standardization recommendations, outputs, and supported applications. The cooperative perception engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure, which complements and accelerates connected and automated vehicle (CAV) technology. The cooperative perception acquires and fuses information from sensors (radar, LiDAR, and cameras) and CAVs to perceive roadway traffic states of moving objects, creates a complete 3D digital representation of that state-space, and communicates it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications. The intelligent roadway infrastructure approach, as opposed to a vehicle-centric approach, is more scalable because it can be deployed to the roughly 300,000 signalized intersections more readily than over 300 million vehicles in the United States, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and greenhouse gas reduction.

ADVANCED PROPULSION SYSTEMS↗
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↗
DOE OSTI2022

Concept of Operations of Next-Generation Traffic Control Utilizing Infrastructure-Based Cooperative Perception: Preprint

This paper puts forth a system architecture for an infrastructure-based cooperative perception (CP) fusion engine, to provide a complete state-space digital representation, with measurable accuracy, to support a wide-range of applications. The architecture includes the inputs, functional flow, data standardization recommendations, outputs and supported applications. The CP engine addresses critical needs with respect to accelerating the benefits of automation through intelligent roadway infrastructure (IRI), that complements and accelerates connected and automated vehicle (CAV) technology. that the CP acquires and fuses information from sensors (radar, LiDAR, and cameras), and CAVs to intelligently perceive roadway traffic states of all moving objects, create a complete three-dimensional digital representation of that state-space, and communicate it to downstream application such as intelligent signal control, safety and energy applications, and cooperate driving applications for CAVs as examples. The IRI approach, as opposed to a vehicle centric approach, is found to be more scalable in that it can deployed to the roughly 300,000 signalized intersections more readily than the over 300 million vehicles in the US, and accrues early-stage benefits equitable to all roadway users addressing safety, equity, fuel efficiency, and GHG reduction.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are at the forefront in advancing the way we interact and perceive with the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as Radar, LiDAR and Video Imaging which are the most popular modalities for ITS. Real-time perception data from these sensors allows intelligent infrastructure side decision making to improve the energy, efficiency and safety at traffic intersections. As traffic departments across the United States are transitioning from traditional loop detectors / emulators and embracing newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception which is reliable, inexpensive, easy to setup and has robust performance in varying weather conditions. However, choosing a sensor which checks all boxes is not straightforward as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long range vehicles and weather resistance but lacks high resolution. LiDAR is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines Radar, LiDAR and camera sensors capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. Through this evaluation, we hope to draw attention to the necessity of National Renewable Energy Laboratory's (NREL) Infrastructure Perception and Control (IPC) framework which presents a multi-sensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like Radar, LiDAR and cameras, offers the most robust solution for enhancing the safety and efficiency in intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Descriptor: Infrastructure Perception and Control: Multi-Sensor Object Tracking Dataset (IPC-MSOT)

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. Traffic accidents often occur at traffic intersections, including a large proportion of traffic fatalities and about one-half of all traffic injuries in the United States. Object detection data were collected in 2024 across three intersections in Colorado Springs, CO, USA, over the course of multiple days and various times to induce a heterogeneous mix of traffic conditions and behaviors. The purpose of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high-resolution, object-level data that can be used for research and development (e.g., to develop multisensor data fusion algorithms). The Infrastructure Perception and Control:Multi-Sensor Object tracking (IPC-MSOT) dataset was collected as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the city of Colorado Springs, Colorado, and the National Renewable Energy Laboratory collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different intersections. This dataset allows for testing of late-stage sensor fusion algorithms and their ability to ingest multimodal sensor data, and it can be utilized by traffic engineers to design and evaluate trajectory-based signal control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Internship Report - Andrew Beathard

This summer, I had the opportunity to work as an intern under Ryan Goldhahn at Lawrence Livermore National Laboratory. Alongside another intern, I was tasked with implementing perception and autonomy algorithms on unmanned aerial systems (UAS) for multiagent cooperative missions and testing their effectiveness at the OS-150 UAS test facility at LLNL. We aimed to engineer a collaborative reaction to a single agent’s identification of an object of interest and test various multiagent algorithms.

42 ENGINEERING↗
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 OSTI2020

Boosting Energy Efficiency of Heterogeneous Connected Automated Vehicle (CAV) Fleets via Anticipative and Cooperative Vehicle Guidance

In 2017, the Department of Energy funded a team at Clemson University and Argonne National Laboratory to develop collaborative perception and anticipative/predictive vehicle guidance schemes for Connected and Automated Vehicles (CAVs) and to quantify the energy saving potential of this technology in large scale traffic microsimulations at different levels of technology penetration and also experimentally. The project goal was demonstrating up to a 10% energy saving potential from different aspects of the implementation with a focus on reducing unnecessary braking events by anticipatory speed and lane selection. This project introduced novel anticipative car following and lane selection schemes for Connected and Automated Vehicles (CAVs). Our control schemes benefited from prediction of human driver behavior, information exchange between CAVs, and sometimes from collaboration to save energy, reduce braking events, and harmonize traffic. The energy savings was first demonstrated by traffic micro-simulations and then via a novel Vehicle-In-the-Loop (VIL) experimental testbed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Equity Considerations in Siting Consolidated Interim Storage Facilities for Commercial Spent Nuclear Fuel

Questions of equity and fairness are often raised by representatives from State, Tribal and local governments and members of the public when resolving conflicts over siting of nuclear facilities. However, the two terms are not synonymous: equity can be quantified while fairness is much more subjective. The Department of Energy's (DOE) December 2021 Request for Information sought input on how to site federal facilities for the temporary, consolidated storage of spent nuclear fuel (SNF) using a consent-based approach. It is anticipated that just such questions about equity and fairness associated with the potential emergence of more than one host site may be raised by the twelve groups of university, nonprofit and private sector partners (Consortia) working to support community engagement, inclusively involve stakeholders, build relationships, and develop innovative forms of mutual learning and public capacity to participate in the consent-based siting process for a federal consolidated interim storage facility during the current Planning & Capacity Building stage of the consent-based siting process. In terms of DOE's Integrated Waste Management System (IWMS), sociopolitical equity concepts are a factor in all aspects of IWMS development and operations and will be particularly applicable should two or more host sites become a possibility, either by the emergence of multiple volunteer hosts or by pursuit as a IWMS program strategy. This paper summarizes recent analyses that explore how four equity metrics - number of states, division of the projected SNF total by the year 2083, current population and land area - might be perceived by host communities, states and regions if two, three or four sites for hosting federal consolidated interim storage facilities were to be contemporaneously realized. Existing institutional arrangements, including Nuclear Regulatory Commission regions, Federal Energy Regulatory Commission regions and Low-Level Radioactive Waste Disposal Compacts, were selected to create hypothetically merged two, three and four-region scenarios to calculate how the four metrics balance out. Projected SNF burden was prioritized with regional contiguity of the consolidated regions a requirement for a functional scenario. The intent was not to promote or suggest any construct as a program objective; the configurations are geopolitical abstractions only to explore potential perceptions of equity. Understanding the possible issues of equity that could arise may benefit program efforts toward achieving a cooperative federalism wherein both the federal and multiple State governments share the goal of manifesting more than one federal consolidated interim storage facility. The main finding is that it is not possible to optimize for all four metrics simultaneously. The analyses demonstrate that trying to create a sense of equity by backfitting a solution to a random population and land distribution will always contain a degree of artificiality.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Improving the Academic Climate of an R1 STEM Department: Quantified Positive Shifts in Perception

Ongoing efforts to improve diversity in science, technology, engineering, and mathematics (STEM) primarily manifest as attempts to recruit more women and individuals from historically marginalized groups. Yet, these efforts fail to repair the specific, systemic issues within academic communities that hinder diverse individuals from persisting and thriving in STEM. Here, we present the results of a quantitative, multiyear effort to make the academic climate of an R1 STEM department more inclusive. We use a student-led, department-specific, faculty-supported initiative to assess and improve the climate of the Department of Chemistry at the University of California, Berkeley, as a case study. Our results provide quantitative evidence that community discussions grounded in our own data, alongside cooperative community efforts to address the issues present in those data, are effective methods for driving positive change. Longitudinal assessment of our academic climate from 2018 to 2020 via annual department-wide surveys indicates that these interventions have succeeded in shifting the perception of our academic climate. This study confirms the positive outcomes of having a practical, sustainable, and data-driven framework for affecting change within a graduate community.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

U.S. and Allied Cyber Security Cooperation in the Indo-Pacific (Workshop Summary)

On March 30, 31, and April 1, 2021, the Center for Global Security Research (CGSR) at Lawrence Livermore National Laboratory (LLNL) hosted a workshop titled "U.S. and allied Cyber Security Cooperation in the Indo-Pacific". This session brought together participants drawn across the policy, military, and private sector in the United States and among allied countries in Europe and the Indo-Pacific. The workshop evaluated threat perceptions, assessed cyber power in a regional context, and explored opportunities for building capacity and trust with allies, partners, and the private sector. The region's cyber threat landscape is marked by increasing malicious activity, with threats persistent and ever-growing. Grounded by this strategic reality, the workshop advocated for 1) a concerted push into the arena of norm setting from the bottom up, 2) an agreed metric by which to assess progress, and 3) the creation of future lines of collaborative effort for the United States and its allies to ensure an open, interoperable, and secure internet.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Advancing Solar Innovation for Low and Moderate-Income Households (Final Technical Report)

The overarching goal of this project was to deepen the understanding of the social and economic aspects of solar adoption, with a specific eye towards any pathways or barriers that could be identified that directly impact low and moderate-income households. Specific objectives included (1) identifying and assessing significant non-economic barriers that complicate the ability of LMI households to adopt and benefit from solar, even under scenarios with low economic barriers; (2) assessing the differential ability of groups to benefit from solar adoption based on their ability to invest in different solar innovations; (3) explaining why some LMI communities nonetheless experience significantly higher rates of solar adoption and benefit than others; (4) developing a methodological model for generating data addressing key issues to the project; and (5) designing and developing an interactive web portal that aggregates existing datasets, provides researchers trend analysis capabilities, standardizes project protocols for other states and communities; provides the opportunities for researchers using the same protocol in other communities to upload their results; and engages with the general public by providing them the ability to upload their own perspectives and stories of solar technology adoption. Through a common framework for conducting semi-structured field interviews. In partnership with cooperative extension services, in conjunction with survey research and the aggregation of existing solar adoption datasets, this study has identified that currently ,there is a strong values alignment and knowledge access challenge that continues to affect solar adoption. Participants in this study – many of whom are either low or moderate-income and/or live in underserved rural communities do not believe solar is an option where they live or believe solar is appropriate for their lifestyle. This perception challenge is further exacerbated by the absolute dearth of adopters in most communities and a total lack of local marketing for solar energy. What marketing that does exist is geared towards audiences other than those in the participant's community, reinforcing a perception that 22% of survey respondents (n = 1,551) have: that solar energy is not meant for them and their community. Overcoming these serious human challenges pertaining to knowledge and marketing are critical to reducing the customer acquisition costs incurred amongst LMI communities and especially those in rural America where the opportunities solar provides to increase local energy resilience, security, all while engaging in personal stewardship of the planet could align with local values. Community-Based Social Marketing, built in partnership with community leaders and geared towards meeting local visions of ideal life, is a near term opportunity to bridge the gap between local priorities and national market and policy imperatives for solar while moving towards the administration's goals for renewable energy as part of a just and equitable decarbonization program. The tools and methods developed for this study can serve as a template for identifying other specific local challenges for solar adoption in other state, and provide the opportunity for a common data framework for understanding system-wide trends and barriers to achieving uptake of residential solar in communities that may, ultimately, benefit the most from the technology.

14 SOLAR ENERGY↗
DOE OSTI2022

Contentious narratives and disinformation about nuclear weapons in strategic deterrence and competition: A SOCOM perspective. Part of Section: United States Special Operations Command (USSOCOM) in Emerging Strategic & Geopolitical Challenges: Operational Implications for US Combatant Commands

Russia’s “special military operation” in Ukraine demonstrates the challenge for strategic deterrence and competition of countering contentious narratives and disinformation about weapons of mass destruction (WMD) during conventional regional wars against a nucleararmed adversary. Moscow uses both tailored, contentious narratives and targeted disinformation about WMD in Ukraine to influence and disrupt local and global perceptions in support of its deterrence and competition objectives vis-à-vis the United States and NATO. Since December 2021, Moscow has made a focal point of chemical, biological, radiological, and nuclear weapons in its efforts to establish a permissive environment for its military buildup on the border with Ukraine and then its military intervention. These information tactics also demonstrate an opportunity for US Special Operations Forces (SOF). They are a case study for considering how SOF can contribute to strategic deterrence and competition objectives, specifically countering adversary gray-zone information efforts to alter regional security orders.1 Such a role is in line with the 2022 Special Operations Forces Vision and Strategy, which provides a framework for the evolution of SOF into “a force capable of creating strategic, asymmetric advantages for the nation as a key contributor of integrated deterrence” (United States Special Operations Command, 2022). This paper briefly examines this strategic challenge and SOF opportunity, focusing narrowly on the distinction between contentious narratives and disinformation about nuclear weapons and the role of SOF in countering these gray-zone information tactics. The nuclear dimension of Moscow’s contentious narratives and disinformation in the “special military operation” is of particular interest because it demonstrates the distinction between strategic efforts to influence and disrupt local and global perceptions in Moscow’s favor. This distinction between influence and disruption is less clear with Russia’s contentious narratives and disinformation about chemical and biological weapons in Ukraine, as disinformation about these two types of WMD appears to overwhelm contentious narratives. We believe this distinction is useful for policymakers and warfighters responsible for countering gray-zone information tactics because it provides a framework for crafting tailored responses to contentious narratives and disinformation about nuclear weapons and other WMD. The chapter concludes with a discussion of efforts that could be undertaken by SOF in cooperation with other relevant stakeholders to address this aspect of adversary gray-zone information tactics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Cutting-Edge Operations Concepts: Intelligent Infrastructure, Cooperative Driving, Signal Control, and Curbside Management

The objective of this breakout session at the NREL Mobility workshop was to examine emerging traffic operations concepts built on key detection technology such as radars, lidars, and imaging recognition - and identify challenges and paths forward to integrate into our current roadway system. The key objective were to identify the opportunities for intelligent infrastructure to support near-term safety, equity, energy, and congestion mitigation goals, while supporting longer term connected-automated vehicle objectives. In the plenary session, Eric Raamot, from the USDOT Highly Automated Safety Systems Office challenged the participants to think broadly about the roll of standards and reference architecture to provide both an accelerated rollout of intelligent infrastructure, but also a repeatable and supportable framework upon which many infrastructure owners and operators (IOOs) could utilize for their implementation purposes.

ADVANCED PROPULSION SYSTEMS↗