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

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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


Recent Submissions

Now showing 1 - 20 of 120
  • Crashworthiness Compatibility Investigation of Autonomous Vehicles with Current Passenger Vehicles
    Dobrovolny, Chiara Silvestri; Stoeltje, Gretchen; Zalani, Aniruddha (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-11)
    Automated Vehicles have been one of the most sought-after concepts to make transportation more effective and safer. No-occupant vehicles with automated driving systems (ADS) make up one such class of vehicles. These are primarily intended for goods transportation services. This vehicle class presents a body structure different than that of a passenger vehicle. Yet, these no-occupant ADS-equipped vehicles are sharing the roads and could potentially be involved in crashes with passenger vehicles. Occupant safety may be compromised if vehicles are not compatible from a crashworthiness perspective. ADS-equipped vehicles should consider appropriate vehicle crashworthiness compatibility given the potential for interactions with vulnerable road users and other vehicle types. Investigation of the level of ADS-equipped vehicle crashworthiness compatibility with human-driven vehicles can lead to more appropriate vehicle designs, as well as more suitable and better passive protection systems for occupants in such crash scenarios. This research project considers finite element crash computer simulation investigation between ADS-equipped and passenger vehicles with the intent to provide a better understanding of the differences in crashworthy behavior of ADS-equipped vehicles.
  • Quantifying the Benefits and Harms of Connected and Automated Vehicle Technologies to Public Health and Equity
    Dadashova, Bahar; Sohrabi, Soheil; Khreis, Haneen; Sener, Ipek; Zmud, Johanna (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-07)
    Automated Vehicles (AVs) have the potential to improve traffic safety by preventing crashes. The safety implications of AVs can vary across communities with different socioeconomic and demographic characteristics. In this study, we proposed a framework to quantify the potential safety implications of AVs in terms of preventable crashes and fatalities, accounting for some of the safety challenges of AV operation, including AV technologies’ safety effectiveness, system failure risk, and the risk of disengagement from the automated system to manual driving. We further defined an empirical study to examine the proposed framework and investigate inequity in AV potential safety implications. The empirical analysis was conducted using 2017 crash data from the Dallas-Fort Worth, Texas, United States area. The results showed that AVs could potentially prevent up to 50%, 46%, 23%, 6%, and 5% of crashes for automation Levels 5 to 1, respectively. Among advanced driver assistance systems, pedestrian detection, electronic stability control, and lane departure warning showed more significant potential in reducing fatal crashes. We found a U-shaped relationship between the AV-preventable fatalities and household median income and ethnically diverse communities. The findings of this study suggests that low-income and ethnically diverse communities can benefit from AV implementation. The policy recommendations of this research suggest that city and state planning and transportation agencies may consider implementing policies and strategies for making AVs available to low-income and ethnically diverse communities at a lower cost.
  • IIHCC Newsletter, April 2021
    (Virginia Tech, 2021-04)
    Each issue of the IIHCC newsletter quickly highlights research, curriculum, facilities, and outreach efforts aligning with the University’s Beyond Boundaries initiative. The newsletters also contain updates regarding meetings, projects, and events associated with IIHCC and its partnerships.
  • Data Mining Twitter to Improve Automated Vehicle Safety
    McDonald, Anthony D.; Huang, Bert; Wei, Ran; Alambeigi, Hananeh; Arachie, Chidubem; Smith, Alexander Charles; Jefferson, Jacelyn (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.
  • Optimize the Communication Cost of 5G Internet of Vehicles through Coherent Beamforming Technology
    Wu, Lan; Xu, Juan; Shi, Lei; Shi, Yi; Zhou, Wenwen (Hindawi, 2021-05-17)
    Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.
  • Improving the Safety of Interactions Between Vulnerable Road Users and Automated Vehicles: A Collaborative Investigation
    Owens, Justin M. (National Surface Transportation Safety Center for Excellence, 2021-04-28)
    This report documents a collaboration between researchers at the Pedestrian Bicycle Information Center at the University of North Carolina’s Highway Safety Research Center and the Virginia Tech Transportation Institute to advance bicycle and pedestrian safety. Efforts focused on (1) exploring and discussing ways to make vulnerable road users (VRUs) safer around existing vehicles with automation; (2) broadening the dialogue surrounding automation and VRU safety to incorporate underrepresented but necessary voices, such as those from advocacy communities; and (3) laying the groundwork to ensure that future automated vehicle (AV) systems, including transitional and fully automated vehicles, are designed with VRU safety as a priority. The collaboration produced a white paper detailing the current state of AV and pedestrian interaction, organized breakout sessions at annual meetings of the Automated Vehicle Symposium, published the results of those sessions, and hosted outreach activities such as webinars and an invited lecture.
  • IIHCC Newsletter, March 2021
    (Virginia Tech, 2021-03)
    Each issue of the IIHCC newsletter quickly highlights research, curriculum, facilities, and outreach efforts aligning with the University’s Beyond Boundaries initiative. The newsletters also contain updates regarding meetings, projects, and events associated with IIHCC and its partnerships.
  • Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles
    Talebpour, Alireza; Lord, Dominique; Manser, Michael P.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to car-following scenarios between AVs and human drivers. Braking and accelerating decision mismatches in this car-following scenario can lead to rear-end near-crashes and therefore warrant further study. This project aims to investigate the behavior of human drivers following an AV leader vehicle in a car-following situation and compare the results with a scenario in which the leader is a vehicle with human-modeled braking behavior. In this study, speed trajectory data was collected from 48 participants using a driving simulator.The results indicated a significant difference between the overall deceleration rates and braking speeds of the participants and the designated AV lead vehicle; however, no such difference was found between the participants and the human-modeled lead vehicle.
  • Modeling Driver Behavior During Automated Vehicle Platooning Failures
    McDonald, Anthony D.; Sarkar, Abhijit; Hickman, Jeffrey S.; Alambeigi, Hananeh; Vogelpohl, Tobias; Markkula, Gustav (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-01)
    Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.
  • 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.
  • IIHCC Newsletter, February 2021
    (Virginia Tech, 2021-02)
    Each issue of the IIHCC newsletter quickly highlights research, curriculum, facilities, and outreach efforts aligning with the University’s Beyond Boundaries initiative. The newsletters also contain updates regarding meetings, projects, and events associated with IIHCC and its partnerships.
  • Estimating Crash Consequences for Occupantless Automated Vehicles
    Witcher, Christina; Henry, Scott; McClafferty, Julie A.; Custer, Kenneth; Sullivan, Kaye; Sudweeks, Jeremy D.; Perez, Miguel A. (Virgina Tech Transportation Institute, 2021-02)
    Occupantless vehicles (OVs) are a proposed application of automated vehicle technology that would deliver goods from merchants to consumers with neither a driver nor passengers onboard. The purpose of this research was to understand and estimate how the increased presence of OVs in the United States fleet may influence crash risk and associated injuries and fatalities. The approach used to estimate potential modifications in crash risk consequences was a counterfactual simulation, where real-world observations were modified as if alternate events had occurred. This analysis leveraged several U.S. national crash databases, along with the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) dataset. The analysis required the derivation of parameters that could be used to modify existing crash estimates as OVs enter the fleet in greater numbers. The team estimated benefit parameters pertaining to (1) the crashes that could be ultimately avoided altogether based on the OV’s smaller size, (2) benefits that could be obtained from the improved crashworthiness characteristics of the OV, and (3) benefits due to the lack of occupants in the OV. Results showed that of the 58,852 fatalities in the national databases examined, a full-scale market penetration of OVs was estimated to reduce fatalities by 34,284, a reduction of 58.2%. Most of this reduction (83%) would come from the lack of occupants in the OVs. Similarly, of the 6,615,117 injured persons in the national databases examined, a full-scale penetration of OVs was estimated to reduce injured persons by 4,088,935, a reduction of 61.8%. As was observed for fatalities, most of this reduction (72.1%) would come from the lack of occupants in the OVs. The results of this investigation, however, should not be taken as definitive benefit estimates. There are important assumptions inherent in the parameters that were used, and some of these assumptions may not be immediately realized. Rather, the results are meant to support critical thinking into how innovative technologies such as OVs may offer benefits that transcend the typical approaches used in vehicle safety, including passive and active safety measures.
  • Real-World Use of Automated Driving Systems and their Safety Consequences: A Naturalistic Driving Data Analysis
    Kim, Hyungil; Song, Miao; Doerzaph, Zachary R. (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Automated driving systems (ADS) have the potential to fundamentally change transportation, and a growing number of these systems have entered the market and are currently in use on public roadways. However, drivers may not use ADS as intended due to misunderstandings about system capabilities and limitations. Moreover, the real-world use and effects of this novel technology on transportation safety are largely unknown. To investigate driver interactions with ADS, we examined existing naturalistic driving data collected from 50 participants who drove personally owned vehicles with partial ADS for 12 months. We found that 47 out of 235 safety-critical events (SCEs) involved ADS use. An in-depth analysis of these 47 SCEs revealed that people misused ADS in 57% of SCEs (e.g., engaged in secondary tasks, used the systems not on highways, or with hands off the wheel). During 13% of SCEs, the ADS neither reacted to the situation nor warned the driver. A post-study survey showed that drivers found ADS useful and usable and felt more comfortable engaging in secondary tasks when ADS were in use. This study also captured some scenarios where the ADS did not meet driver expectations. The findings of this report may help inform the development of human-machine interfaces and training programs and provide awareness of the potential for unintended use of ADS and their associated safety consequences.
  • 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.
  • IIHCC Newsletter, December 2020
    (Virginia Tech, 2020-12)
    Each issue of the IIHCC newsletter quickly highlights research, curriculum, facilities, and outreach efforts aligning with the University’s Beyond Boundaries initiative. The newsletters also contain updates regarding meetings, projects, and events associated with IIHCC and its partnerships.
  • IIHCC Newsletter, October 2020
    (Virginia Tech, 2020-10)
    Each issue of the IIHCC newsletter quickly highlights research, curriculum, facilities, and outreach efforts aligning with the University’s Beyond Boundaries initiative. The newsletters also contain updates regarding meetings, projects, and events associated with IIHCC and its partnerships.
  • CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets
    Pipattanasomporn, Manisa; Chitalia, Gopal; Songsiri, Jitkomut; Aswakul, Chaodit; Pora, Wanchalerm; Suwankawin, Surapong; Audomvongseree, Kulyos; Hoonchareon, Naebboon (2020-07-20)
    This paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m(2) office building located in Bangkok, Thailand. The electricity consumption data (kW) are that of individual air conditioning units, lighting, and plug loads in each of the 33 zones of the building. The indoor environmental sensor data comprise temperature (degrees C), relative humidity (%), and ambient light (lux) measurements of the same zones. The entire datasets are available at one-minute intervals for the period of 18 months from July 1, 2018, to December 31, 2019. Such datasets can be used to support a wide range of applications, such as zone-level, floor-level, and building-level load forecasting, indoor thermal model development, validation of building simulation models, development of demand response algorithms by load type, anomaly detection methods, and reinforcement learning algorithms for control of multiple AC units.
  • IIHCC Newsletter: Summer Edition, August 2020
    (Virginia Tech, 2020-08)
    Each issue of the IIHCC newsletter quickly highlights research, curriculum, facilities, and outreach efforts aligning with the University’s Beyond Boundaries initiative. The newsletters also contain updates regarding meetings, projects, and events associated with IIHCC and its partnerships.
  • Formalizing Human Machine Communication in the Context of Autonomous Vehicles
    Gopalswamy, Swaminathan; Saripalli, Srikanth; Shell, Dylan; Hickman, Jeffrey S.; Hsu, Ya-Chuan (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-05)
    There are many situations where tacit communication between drivers and pedestrians governs and enhances safety. The goal of this study was to formalize this communication and apply it to the driving strategy of an autonomous vehicle. Toward this, we performed a field study of the interaction between drivers and pedestrians. Vehicles were instrumented to capture behavioral information on a driver as well as passengers and the traffic scenario in general. The data captured were reduced by data analysts to provide insights into the communication and driving patterns. The categorical reduction on driver, pedestrian, and environmental variables was captured. A domain specific language (DSL) was developed to precisely describe the driver-pedestrian behavior, toward the development of a behavioral model for generating autonomous vehicle controls. Specifically, interaction was formalized through a probabilistic model, namely a partially observable Markov decision process (POMDP). This enabled study of what-if scenarios with different risk averseness characteristics. One particular strategy was implemented on an autonomous vehicle and experimental observations were made. Future work will consider (i) richer DSLs to better quantify the driver-human communication, (ii) faster POMDP solvers for real-time operation, and (iii) further applications.
  • Standardized Performance Evaluation of Vehicles with Automated Capabilities
    Basantis, Alexis; Harwood, Leslie C.; Doerzaph, Zachary R.; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-12)
    Advanced driver-assistance systems (ADAS) are becoming widely available in the new vehicle landscape, increasing of both vehicle occupants’ and other road users’ safety. In some vehicles, longitudinal and lateral positioning under certain conditions can be maintained, designating them as having either SAE level 1 (L1) or level 2 (L2) automated features. By developing a standardized set of tests to be applied to current L1 and L2 vehicles, while keeping the future advancement of automation in mind, these vehicles’ system performance, feature limitations, and performance consistency can be systematically evaluated. This project sought to develop an easily implementable, standardized set of testing procedures that could be quickly and inexpensively performed on automated vehicles to characterize their feature capabilities and limitations. Such information is useful to private or public organizations interested in a standardized approach to classifying vehicle capabilities, whether for informing the expectation of operators, or for cataloging and learning from the variety of implementation alternatives. Although not the primary purpose, this project may also help inform efforts to develop certification or other standardized vehicle performance efforts. The results of this project showed that specific roadway factors affected automated feature performance and that there was significant performance variability across test vehicles.