Destination Area: Intelligent Infrastructure for Human-Centered Communities (IIHCC)

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https://hdl.handle.net/10919/79980

IIHCC focuses its attention on the ways that people interact with one another and with their environment. Interest areas in this DA include smart, healthy, and sustainable cities and communities; transportation systems; human safety, health, and wellness; integrated energy systems; network science and engineering; public policy; and cyber-physical systems. The initial focus for IIHCC will be on four themes: Ubiquitous Mobility: The location-agnostic promise of new communication and information technologies Automated Vehicle Systems: vehicles that can transit safely and efficiently through our communities independent of a human operator Smart Design and Construction: an intelligent, integrated, adaptable, responsive, and sustainable human-centric built environment Energy: the underlying innovations that will be required in the production, distribution, and consumption of energy to realize such a system

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3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles
Maurya, Deepam; Khaleghian, Seyedmeysam; Sriramdas, Rammohan; Kumar, Prashant; Kishore, Ravi Anant; Kang, Min-Gyu; Kumar, Vireshwar; Song, Hyun-Cheol; Lee, Seul-Yi; Yan, Yongke; Park, Jung-Min (Jerry); Taheri, Saied; Priya, Shashank (2020-10-26)
The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability. Designing efficient sensors for smart tires for autonomous vehicles remains a challenge. Here, the authors present a tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis.
Adoption of High-Performance Housing Technologies Among U.S. Homebuilding Firms, 2000 Through 2010
McCoy, Andrew P.; Koebel, C. Theodore; Sanderford, Andrew R.; Franck, Christopher T.; Keefe, Matthew J. (HUD, 2015)
This article describes foundational processes of a larger project examining U.S. home builders’ choices to adopt innovative housing technologies that improve the environmental performance of new single-family homes. Home builders sit at a critical juncture in the housing creation decision chain and can influence how new housing units change related to energy consumption, and the units they produce can also reflect shifting technology, demography, and policy landscapes. With some exceptions, U.S. home builders have been characterized as being slow to adopt or resistant to the adoption of product and process innovations, largely because of path-dependent and risk-averse behavior. This article focuses on home builder choices by analyzing a summary of innovation adoption literature and that literature’s relationship to homebuilding. Researchers then describe analytical approaches for studying home builders’ choices and markets at a Core Based Statistical Area level, the data and statistical methodologies used in the study, and the policy implications for promoting energy efficiency in housing. Future work will draw on the foundation presented in this article to specify versions of this generic model and report results using improved quantitative analyses.
AERIS : Eco-Vehicle Speed Control at Signalized Intersections Using I2V Communication
Rakha, Hesham A.; Kamalanathsharma, Raj Kishore; Ahn, Kyoungho (United States. Joint Program Office for Intelligent Transportation Systems, 2012-06)
This report concentrates on a velocity advisory tool, or decision support system, for vehicles approaching an intersection using communication capabilities between the infrastructure and vehicles. The system uses available signal change information, vehicle characteristics, lead vehicle characteristics, and intersection features to compute the fuel-optimal vehicle trajectory. The proposed system involves a complex optimization logic incorporating roadway characteristics, lead vehicle information, vehicle acceleration capabilities and microscopic fuel consumption models to generate a fuel-optimal speed profile. The research also develops a MATLAB application in order to demonstrate the potential of an in-vehicle application of such a technology.
Agent-Based Game Theory Modeling for Driverless Vehicles at Intersections
Rakha, Hesham A.; Zohdy, Ismail H.; Kamalanathsharma, Raj Kishore (United States. Department of Transportation, 2013-02-19)
This report presents three research efforts that were published in various journals. The first research effort presents a reactive-driving agent based algorithm for modeling driver left turn gap acceptance behavior at signalized intersections. This model considers the interaction between driver characteristics and vehicle physical capabilities. The model explicitly captures the vehicle constraints on driving behavior using a vehicle dynamics model. In addition, the model uses the driver's input and the psychological deliberation in accepting/rejecting a gap. The model is developed using a total of 301 accepted gaps and subsequently validated using 2,429 rejected gaps at the same site and also validated using 1,485 gap decisions (323 accepted and 1,162 rejected) at another site. The proposed model is considered as a mix between traditional and reactive methods for decision making and consists of three main components: input, data processing and output. The input component uses sensing information, vehicle and driver characteristics to process the data and estimate the critical gap value. Thereafter, the agent decides to either accept or reject the offered gap by comparing to a driver-specific critical gap (the offered gap should be greater than the critical gap for it to be accepted). The results demonstrate that the agent-based model is superior to the standard logistic regression model because it produces consistent performance for accepted and rejected gaps (correct predictions in 90% of the observations) and the model is easily transferable to different sites. The proposed modeling framework can be generalized to capture different vehicle types, roadway configurations, traffic movements, intersection characteristics, and weather effects on driver gap acceptance behavior. The findings of this research effort is considered as an essential stage for modeling autonomous/driverless vehicles The second effort develops a heuristic optimization algorithm for automated vehicles (equipped with cooperative adaptive cruise control CACC systems) at uncontrolled intersections using a game theory framework. The proposed system models the automated vehicles as reactive agents interacting and collaborating with the intersection controller (manager agent) to minimize the total delay. The system is evaluated using a case study considering two different intersection control scenarios: a four-way stop control and the proposed intersection controller framework. In both scenarios, four automated vehicles (a single vehicle per approach) were simulated using a Monte Carlo simulation that was repeated 1000 times. The results show that the proposed system reduces the total delay relative to a traditional stop control by 35 seconds on average, which corresponds to an approximately 70 percent reduction in the total delay. The third effort presents a new tool for optimizing the movements of autonomous/driverless vehicles through intersections: iCACC. The main concept of the proposed tool is to control vehicle trajectories using Cooperative Adaptive Cruise Control (CACC) systems to avoid collisions and minimize intersection delay. Simulations were executed to compare conventional signal control with iCACC considering two measures of effectiveness - delay and fuel consumption. Savings in delay and fuel consumption in the range of 91 and 82 percent relative to conventional signal control were demonstrated, respectively. It is anticipated that the findings of this report may contribute in the future of advanced vehicles control and connected vehicles applications.
Ambient ammonia synthesis via palladium-catalyzed electrohydrogenation of dinitrogen at low overpotential
Wang, Jun; Yu, Liang; Hu, Lin; Chen, Gang; Xin, Hongliang; Feng, Xiaofeng (Springer Nature, 2018-05-15)
Electrochemical reduction of N2 to NH3 provides an alternative to the Haber−Bosch process for sustainable, distributed production of NH3 when powered by renewable electricity. However, the development of such process has been impeded by the lack of efficient electrocatalysts for N2 reduction. Here we report efficient electroreduction of N2 to NH3 on palladium nanoparticles in phosphate buffer solution under ambient conditions, which exhibits high activity and selectivity with an NH3 yield rate of ~4.5 μg mg−1Pd h−1 and a Faradaic efficiency of 8.2% at 0.1 V vs. the reversible hydrogen electrode (corresponding to a low overpotential of 56 mV), outperforming other catalysts including gold and platinum. Density functional theory calculations suggest that the unique activity of palladium originates from its balanced hydrogen evolution activity and the Grotthuss-like hydride transfer mechanism on α-palladium hydride that lowers the free energy barrier of N2 hydrogenation to *N2H, the rate-limiting step for NH3 electrosynthesis.
Applications of Connected Vehicle Infrastructure Technologies to Enhance Transit Service Efficiency and Safety, Part 1
Hancock, Kathleen L. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-09-30)
Implementing Connected Vehicle Infrastructure (CVI) applications for handheld devices into public transportation transit systems would provide transit agencies and their users with two-directional information flow from traveler-to-agencies, agencies-to-traveler, traveler-to-vehicle, and vehicle-to-traveler. This information flow could improve the efficiency of services provided by the agency and enhance the safety of travelers and drivers. This project developed an architectural framework for two CVI applications: (1) an application for dynamic demand-response transit (DRT) services and (2) an enhanced traveler safety application that allows individuals to notify a transit vehicle that they are within a specified distance of the vehicle’s current stop location. A limited simulation was performed to evaluate the potential of using this location information with respect to a transit vehicle to provide flexibility for that vehicle to remain at a stop for a limited time, minimizing passenger wait time and exposure to potential safety issues, specifically during night operations. An annotated bibliography of resources used for this study is also provided.
Applications of Connected Vehicle Infrastructure Technologies to Enhance Transit Service Efficiency and Safety, Part 2
Lee, Young-Jae; Thomas, Clayton; Dadvar, Seyedehsan (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-09-30)
Many transit agencies provide real-time operational information and trip-planning tools through phone, Web, and smartphone applications. These services utilize a one-way information flow from transit agencies to transit users. Current smartphone technology and connected vehicle infrastructure (CVI), however, can allow a two-directional information flow from users to transit agencies and back. This report provides a literature review on the state of current transit apps; proposes a system architecture for a smartphone app that allows for dynamic flexible routing and increased transit user safety; and presents the results of a survey conducted on the perception and acceptability of the model app. Survey results were analyzed in terms of safety, efficiency, and privacy for different demographic, travel behavior, and geographic characteristics. Results showed that users did not significantly consider the privacy issues (7.1 on a scale from 1 [least acceptable] to 10 [most acceptable]) but believed that it could improve nighttime safety (7.3/10.0). Users believed that the app could improve nighttime pedestrian safety if it were connected to the police department (7.8/10.0). This app was also expected to improve transit efficiency and increase ridership, and is eventually recommendable (7.3/10.0). The least expected improvement was daytime safety (6.4/10.0), which is reasonable and expectable.
Assessment of a Drowsy Driver Warning System for Heavy Vehicle Drivers: Final Report
Olson, Rebecca Lynn; Morgan, Justin F.; Hanowski, Richard J.; Daily, Brian; Zimmermann, Richard P.; Blanco, Myra; Bocanegra, Joseph L.; Fitch, Gregory M.; Flintsch, Alejandra Medina (United States. National Highway Traffic Safety Administration, 2008)
Drowsiness has a globally negative impact on performance, slowing reaction time, decreasing situational awareness, and impairing judgment. A field operational test of an early prototype Drowsy Driver Warning System was conducted as a result of 12 years of field and laboratory studies by the National Highway Traffic Administration and the Federal Motor Carrier Safety Administration. This project included Control and Test groups. The final data set for the analysis consisted of 102 drivers from 3 for-hire trucking fleets using 46 instrumented trucks. Fifty-seven drivers were line-haul and 45 were long-haul operators. The data set contained nearly 12.4 terabytes of truck instrumentation data, kinematic data, and video recordings for 2.4 million miles of driving and 48,000 driving-data hours recorded, resulting in the largest data set ever collected by the U.S. Department of Transportation. In this study, 53 research questions were addressed related to safety benefits, acceptance, and deployment. Novel data reduction procedures and data analyses were used. Results showed that drivers in the Test Group were less drowsy. Drivers with favoring opinions of the system tended to have an increase in safety benefits. Results of the assessment revealed that the early prototype device had an overall positive impact on driver safety.
An Assessment of Quiet Vehicles and Pedestrian and Bicyclist Safety
Alden, Andrew S. (National Surface Transportation Safety Center for Excellence, 2014-07-28)
The primary intent of this report is to provide a comprehensive and concise overview of the apparent safety issues presented to pedestrians and pedalcyclists by the operation of quiet vehicles on roadways. The report provides background information to establish how this issue became the focus of safety research in the United States and elsewhere. It presents the findings of a literature review of notable major research and a review of related pending and established regulations. The report also describes implemented and proposed countermeasure methods in addition to opportunities for future potential research to address knowledge gaps and improve overall understanding of the issues.
Automated Vehicle Crash Rate Comparison Using Naturalistic Data
Blanco, Myra; Atwood, Jon; Russell, Sheldon M.; Trimble, Tammy E.; McClafferty, Julie A.; Perez, Miguel A. (Virginia Tech Transportation Institute, 2016-01-08)
This study assessed driving risk for the United States nationally and for the Google Self-Driving Car project. Driving safety on public roads was examined in three ways. The total crash rates for the Self-Driving Car and the national population were compared to (1) rates reported to the police, (2) crash rates for different types of roadways, and (3) scenarios that give rise to unreported crashes. First, crash rates from the Google Self-Driving Car project per million miles driven, broken down by severity level were calculated. The Self-Driving Car rates were compared to rates developed using national databases which draw upon police-reported crashes and rates estimated from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS). Second, SHRP 2 NDS data were used to calculate crash rates for three levels of crash severity on different types of roads, broken down by the speed limit and geographic classification (termed “locality” in the study; e.g., urban road, interstate). Third, SHRP 2 NDS data were again used to describe various scenarios related to crashes with no known police report. This analysis considered whether such factors as driver distraction or impairment were involved, or whether these crashes involved rear-end collisions or road departures. Crashes within the SHRP 2 NDS dataset were ranked according to severity for the referenced event/incident type(s) based on the magnitude of vehicle dynamics (e.g., high Delta-V or acceleration), the presumed amount of property damage (less than or greater than $1,500, airbag deployment), knowledge of human injuries (often unknown in this dataset), and the level of risk posed to the drivers and other road users (Antin, et al., 2015; Table 1). Google Self-Driving Car crashes were also analyzed using the methods developed for the SHRP 2 NDS in order to determine crash severity levels and fault (using these methods, none of the vehicles operating in autonomous mode were deemed at fault in crashes).
Autonomous Emergency Navigation to a Safe Roadside Location
Furukawa, Tomonari; Zuo, Lei; Parker, Robert G.; Yang, Lisheng (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
In this project, we developed essential modules for achieving the proposed autonomous emergency navigation function for an automated vehicle. We investigated and designed sensing solutions for safe roadside location identification, as well as control solutions for autonomous navigation to the identified location. Sensing capabilities are achieved by advanced fusion algorithms of 3D Lidar and stereo camera data. A novel control design, based on dynamic differential programming, was developed to efficiently plan navigation trajectories while dealing with computation delay and modelling errors. Preliminary validation of proposed solutions was carried out in a simulated environment. The results show strong potential for success, especially for the control module. Hardware integration in a real vehicle has been ongoing in a parallel fashion to enable field tests of developed modules in future work. Key sensing equipment was installed and calibrated and used to collect data for offline analysis. The retrofitting of the vehicle’s actuation mechanism was finished with the whole drive-by-wire system in place. Future work will involve road testing the developed systems.
Bioinspired Tracking Control of High Speed Nonholonomic Ground Vehicles
Shoemaker, Adam; Leonessa, Alexander (Hindawi, 2015-10-04)
The behavior of nature’s predators is considered for designing a high speed tracking controller for nonholonomic vehicles, whose dynamicsare represented using a unicycle model. To ensure that the vehicle behaves intuitively and mimics the biologically inspiredpredator-prey interaction, saturation constraints based on Ackermann steering kinematics are added. A new strategy for mapping commandsback into a viable envelope is introduced, and the restrictions are accounted for using Lyapunov stability criteria. Followingverification of the saturation constraints, the proposed algorithm was implemented on a testing platform. Stable trajectories of up to 9 m/swere achieved. The results presented show that the algorithm demonstrates significant promise in high speed trajectory tracking withobstacle avoidance.
The CEHMS Chronicle, April 2014
(Virginia Tech, 2014-04)
This is the quarterly newsletter for the Center for Energy Harvesting Materials and Systems.
The CEHMS Chronicle, November 2013
(Virginia Tech, 2013-11)
This is the quarterly newsletter for the Center for Energy Harvesting Materials and Systems.
Choices and Challenges 2019: Self-driving Cars in the New River Valley
Choices and Challenges (Virginia Tech, 2019-04-04)
The Choices and Challenges Forum brochure includes a schedule of events held April 4, 2019, at the Inn at Virginia Tech. This event seeks to explore the choices we still face and the challenges raised by the potential presence of self-driving cars on our roads and in our lives. While most thinking about and planning for self-driving cars has focused on urban environments, the New River Valley’s mixture of small town and rural communities offers a unique opportunity for exploring a range of questions. Will these vehicles interfere with our privacy? Are they safe, for passengers, pedestrians, bicyclists, and others? Will they undermine existing public transportation options, or expand public transportation into new communities? Will they increase or decrease transportation accessibility for poor communities, the elderly, and the disabled? Will they shift patterns of vehicle ownership? Will they improve traffic or create new problems? Will they require new regulations, and if so, by whom and of what sort? Are they ultimately good or bad for our environment, our communities, and our personal lives? Our Choices and Challenges forum will bring together internationally-recognized experts and the public to discuss these and other important questions.
City-Wide Eco-Routing Navigation Considering Vehicular Communication Impacts
Elbery, Ahmed; Rakha, Hesham A. (MDPI, 2019-01-12)
Intelligent Transportation Systems (ITSs) utilize Vehicular Ad-hoc Networks (VANETs) to collect, disseminate, and share data with the Traffic Management Center (TMC) and different actuators. Consequently, packet drop and delay in VANETs can significantly impact ITS performance. Feedback-based eco-routing (FB-ECO) is a promising ITS technology, which is expected to reduce vehicle fuel/energy consumption and pollutant emissions by routing drivers through the most environmentally friendly routes. To compute these routes, the FB-ECO utilizes VANET communication to update link costs in real-time, based on the experiences of other vehicles in the system. In this paper, we study the impact of vehicular communication on FB-ECO navigation performance in a large-scale real network with realistic calibrated traffic demand data. We conduct this study at different market penetration rates and different congestion levels. We start by conducting a sensitivity analysis of the market penetration rate on the FB-ECO system performance, and its network-wide impacts considering ideal communication. Subsequently, we study the impact of the communication network on system performance for different market penetration levels, considering the communication system. The results demonstrate that, for market penetration levels less than 30%, the eco-routing system performs adequately in both the ideal and realistic communication scenarios. It also shows that, for realistic communication, increasing the market penetration rate results in a network-wide degradation of the system performance.
A collaboration workflow from sound-based composition to performance of electroacoustic music using «Pure Data» as a framework
Tsoukalas, Kyriakos D. (Bauhaus Universitat Weimer, 2011-08)
This paper describes a workflow for composers, engineers and performers to collaborate, using Pure Data (PD) as a framework, towards the design of electroacoustic musical instruments intended for live performances of sound-based music. Furthermore, it presents some considerations about live performance and ideas of creating collaboration tools, possibly as PD GUI plugins.
Connected Motorcycle Crash Warning Interfaces
Song, Miao; McLaughlin, Shane B.; Doerzaph, Zachary R. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-01-15)
Crash warning systems have been deployed in the high-end vehicle market segment for some time and are trickling down to additional motor vehicle industry segments each year. The motorcycle segment, however, has no deployed crash warning system to date. With the active development of next generation crash warning systems based on connected vehicle technologies, this study explored possible interface designs for motorcycle crash warning systems and evaluated their rider acceptance and effectiveness in a connected vehicle context. Four prototype warning interface displays covering three warning mode alternatives (auditory, visual, and haptic) were designed and developed for motorcycles. They were tested on-road with three connected vehicle safety applications - intersection movement assist, forward collision warning, and lane departure warning - which were selected according to the most impactful crash types identified for motorcycles. It showed that a combination of warning modalities was preferred to a single display by 87.2% of participants and combined auditory and haptic displays showed considerable promise for implementation. Auditory display is easily implemented given the adoption rate of in-helmet auditory systems. Its weakness of presenting directional information in this study may be remedied by using simple speech or with the help of haptic design, which performed well at providing such information and was also found to be attractive to riders. The findings revealed both opportunities and challenges of visual displays for motorcycle crash warning systems. More importantly, differences among riders of three major motorcycle types (cruiser, sport, and touring) in terms of riders’ acceptance of a crash warning interface were revealed. Based on the results, recommendations were provided for an appropriate crash warning interface design for motorcycles and riders in a connected vehicle environment.
Connected Motorcycle System Performance
Viray, Reginald; Noble, Alexandria M.; Doerzaph, Zachary R.; McLaughlin, Shane B. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-01-15)
This project characterized the performance of Connected Vehicle Systems (CVS) on motorcycles based on two key components: global positioning and wireless communication systems. Considering that Global Positioning System (GPS) and 5.9 GHz Dedicated Short-Range Communications (DSRC) may be affected by motorcycle rider occlusion, antenna mounting configurations were investigated. In order to assess the performance of these systems, the Virginia Tech Transportation Institute’s (VTTI) Data Acquisition System (DAS) was utilized to record key GPS and DSRC variables from the vehicle’s CVS Vehicle Awareness Device (VAD). In this project, a total of four vehicles were used where one motorcycle had a forward mounted antenna, another motorcycle had a rear mounted antenna, and two automobiles had centermounted antennas. These instrumented vehicles were then subject to several static and dynamic test scenarios on closed test track and public roadways to characterize performance against each other. Further, these test scenarios took into account motorcycle rider occlusion, relative ranges, and diverse topographical roadway environments. From the results, both rider occlusion and approach ranges were shown to have an impact on communications performance. In situations where the antenna on the motorcycle had direct lineof-sight with another vehicle’s antenna, a noticeable increase in performance can be seen in comparison to situations where the line of sight is occluded. Further, the forward-mounted antenna configuration provided a wider span of communication ranges in open-sky. In comparison, the rear-mounted antenna configuration experienced a narrower communication range. In terms of position performance, environments where objects occluded the sky, such as deep urban and mountain regions, relatively degraded performance when compared to open sky environments were observed.
Connected Vehicle Applications for Adaptive Overhead Lighting (On-demand Lighting)
Gibbons, Ronald B.; Palmer, Matthew; Jahangiri, Arash (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-07-01)
The Virginia Tech Transportation Institute (VTTI) has developed an on-demand roadway lighting system and has tested the system’s effect on driver visual performance. On-demand roadway lighting can dramatically reduce energy usage while maintaining or increasing vehicle and pedestrian safety. The system developed by VTTI uses connected vehicle technology (CVT), wireless lighting controls, LED luminaires, and a stand-alone processor on the Virginia Smart Road to sense vehicles and turn on roadway lighting only when needed. During this research project, the use of on-demand, or just-in-time, lighting was investigated with respect to assessing driver distraction, and to human factors, including a driver’s ability to visually detect and recognize on-road objects and pedestrians. The developed on-demand lighting system described above utilized dedicated short range communication (DSRC), connected vehicle infrastructure (CVI), and centralized wireless lighting controls, and was used with VTTI-developed in-vehicle instrumentation and custom software. The software allowed the study of forward preview time in terms of forward lighting distance needed for drivers to detect roadside pedestrians and hazards. Visual performance testing revealed a relationship between speed and the amount of forward lighting needed to detect pedestrians and hazards on the side of the roadway, and a small, but statistically insignificant, practical difference in visual performance between on-demand lighting and continuously-on lighting conditions. A survey of participant reactions indicated that the public generally accepts on-demand lighting and does not find it distracting as long as a minimum lighting condition is met. The survey also found that participants felt the system provided a safe driving environment. The main application for an on-demand lighting system would be on roadways with little traffic at night and higher accident rates, or higher conflict areas such as intersections, pedestrian crossings, and merge areas.

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