Browsing by Author "Mollenhauer, Michael A."

Now showing 1 - 20 of 22
  • Thumbnail Image
    Aerial Traffic
    Viray, Reginald; Saffy, Joshua; Mollenhauer, Michael A. (National Surface Transportation Safety Center for Excellence, 2024-08-30)
    This report documents a significant advancement in work-zone safety through the strategic integration of aerial drone technology and machine vision software. It summarizes the project’s phases: Technical Assessment and Procurement, System Integration and Validation, and Deployment Assessment. The Technical Assessment and Procurement phase led to the selection of Smartek ITS’s DataFromSky product for its unique real-time processing capabilities of aerial drone video, making it superior to other commercial offerings. The System Integration and Validation phase ensured that the video streams, whether real-time or recorded, were processed effectively for varying roadway scenarios, including work zones and intersection monitoring. Accompanying development work included a user-defined data interface with the capability to trigger intruding vehicle alarms. The Deployment Assessment phase confirmed the system’s precision, with object detection up to 150 meters and sub-500 millisecond latency in relaying data for real-time alerts. Despite some GPS data discrepancies due to wind-induced drone movements, the system showed promise in controlled and real-world environments. Overall, the project acquired and validated the system’s functionality, with successful tests on live and recorded video feeds, software video processing, and real-time data transmission, culminating in the development of a robust intruding vehicle alarm mechanism. The system demonstrated great potential for deployment across various Virginia Tech Transportation Institute research initiatives, setting a precedent for future work in enhancing work zone safety.
  • Thumbnail Image
    Automated Truck Mounted Attenuator: Phase 2 Performance Measurement and Testing
    Vilela, Jean Paul Talledo; Mollenhauer, Michael A.; White, Elizabeth E.; Vaughn, Elijah W. (Safe-D University Transportation Center, 2023-12)
    Truck-Mounted Attenuators (TMAs) are energy-absorbing devices added to heavy shadow vehicles to provide a mobile barrier that protects work crews from errant vehicles entering active work zones. In mobile and short duration operations, drivers manually operate the TMA, keeping pace with the work zone as needed to function as a mobile barrier protecting work crews. While the TMA is designed to absorb and/or redirect the energy from a colliding vehicle, there is still significant risk of injury to the TMA driver when struck. TMA crashes are a serious problem in Virginia, where they have increased each year from 2011 (17 crashes) to 2014 (45 crashes), despite a decrease in the number of active construction sites between 2013 and 2014. Although various efforts have been made to improve TMA vehicle crashworthiness (e.g., by adding interior padding, harnesses, and supplemental head restraints), the most effective way to protect TMA drivers may be to remove them from the vehicle altogether. Recent advances in automated vehicle technologies—including advanced sensing, high-precision differential GPS, inertial sensing, advanced control algorithms, and machine learning—have enabled the development of automated systems capable of controlling TMA vehicles. Furthermore, the relatively low operating speeds and platoon-like operating movements of leader-follower TMA systems make an automated control concept feasible for a variety of mobile and short-duration TMA use cases without the cost or complexity of full autonomy. This project seeks to develop an automated control system for TMA vehicles using a short following distance, leader-follower control concept which will remove the driver from the at-risk TMA.
  • Thumbnail Image
    Changes in Travel Behavior, Attitudes, and Preferences among E-Scooter Riders and Non-Riders: A First Look at Results from Pre and Post E-Scooter System Launch Surveys at Virginia Tech
    Buehler, Ralph; Broaddus, Andrea; Swenney, Ted; Mollenhauer, Michael A.; White, Elizabeth; Zhang, Wenwen (2021-04-22)
    Shared micromobility such as electric scooters (e-scooters) has potential to enhance the sustainability of urban transport by displacing car trips, providing more mobility options, and improving access to public transit. Most published studies on e-scooter ridership focus on cities and only capture data at one point in time. This study reports results from two cross-sectional surveys deployed before (n=462) and after (n=428) the launch of a fleet of shared e-scooters on Virginia Tech’s campus in Blacksburg, VA. This allowed for a pre-post comparison of attitudes and preferences of e-scooter riders and non-users. E-scooter ridership on campus follows patterns identified in other studies, with a greater share of younger riders—in particular undergraduate students. Stated intention to ride prior to system launch was greater than actual ridership after system launch. The drop-off between pre-launch intention to ride and actual riding was strongest for older age groups, women, and university staff. As in city surveys, the main reasons for riding e-scooters on campus were travel speed and fun of riding. About 30% indicated using e-scooters to ride to parking lots or to access public transport service—indicating e-scooters’ potential as connector to other modes of transport. Compared to responses prior to system launch, perceptions about the convenience, cost, safety, parking, rider behavior, and usefulness of the e-scooter systems were more positive among non-riders after system launch—indicating that pilot projects may improve public perception of e-scooters. Building more bike lanes or separate spaces for e-scooters to ride could help move e-scooter riders off sidewalks—a desire expressed by both pedestrians and e-scooter users.
  • Thumbnail Image
    Design and Development of an Automated Truck Mounted Attenuator
    White, Elizabeth E.; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    Truck-Mounted Attenuators (TMAs) are energy-absorbing devices added to heavy shadow vehicles to provide a mobile barrier that protects work crews from errant vehicles entering active work zones. While the TMA is designed to absorb and/or redirect the energy from a colliding vehicle, there is still significant risk of injury to the TMA driver when struck, which has happened at an increasing rate in Virginia since 2011. Although various efforts have been made to improve TMA driver crashworthiness, the most effective way to protect TMA drivers may be to remove them from the vehicle altogether. During this project, a consortium consisting of VTTI, VDOT, DBi Services, and Transurban collaborated to design and build an automated TMA system (ATMA) that will remove the driver in future phases from the TMA vehicle in mobile and short duration work zone operations using a short following distance leader-follower control concept. The resulting ATMA successfully operates at speeds up to 15mph in environments with dependable GPS signal and at commanded following distances between 50-400 feet. The ATMA features a LIDAR-based system to detect and respond to obstacles and has an extensive internal and external human-machine interface to support communications between system operators and external road users.
  • Thumbnail Image
    Design and Evaluation of a Connected Work Zone Hazard Detection and Communication System for Connected and Automated Vehicles (CAVs)
    Mollenhauer, Michael A.; White, Elizabeth E.; Roofigari-Esfahan, Nazila (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-08)
    Roadside work zones (WZs) present imminent safety hazards for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes, which are the second most common cause of worker fatalities. The advent of connected and connected automated vehicles (CVs/CAVs) is driving WZ safety practitioners and vehicle designers towards implementing solutions that will more accurately describe activity in WZs to help identify and communicate imminent safety hazards that elevate crash risks. A viable solution to this problem is to accurately localize, monitor, and predict WZ actors’ collision threats based on their movements and activities. This information along with CV/CAVs’ trajectories can be used to detect potential proximity conflicts and provide advanced warnings to workers, passing drivers, and CAV control systems. This project aims to address WZ safety by delivering a real-time threat detection and warning algorithm that can be used in wearable WZ communication solutions in conjunction with CVs/CAVs. As a result, this research provides a key element required to significantly improve the safety conditions of roadside WZs through prompt detection and communication of hazardous situations to workers and CVs/CAVs alike.
  • Thumbnail Image
    Development of a Connected Smart Vest for Improved Roadside Work Zone Safety
    Roofigari-Esfahan, Nazila; White, Elizabeth E.; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-04)
    Roadside work zones (WZs) present imminent safety threats for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes. A number of factors (aging highway infrastructure, increased road work, increased levels of traffic and more nighttime WZs) have led to an increase in WZ crashes in the past few years. The standard WZ safety signage and personal protective equipment worn by workers at roadside WZs have not been completely effective in controlling WZ crashes. This project aims to address this issue by designing a wearable device to accurately localize, monitor, and predict potential collisions between WZ actors based on their movements and activities, and communicate potential collisions to workers, passing drivers, and connected and automated vehicles (CAVs). Through this project, a wearable worker localization and communication device (i.e., Smart Vest) was developed that utilizes the previously developed Threat Detection Algorithm to communicate workers’ locations to passing CAVs and proactively warn workers and passing motorists of potential collisions. As a result, this research is expected to significantly improve the safety conditions of roadside WZs through prompt detection and communication of hazardous situations to workers and drivers.
  • Thumbnail Image
    Development of Human Factors Guidelines for ATIS and CVO Identify Strengths and Weaknesses of Alternative Information Display Formats
    Hulse, Melissa C.; Dingus, Thomas A.; Mollenhauer, Michael A.; Liu, Y. C.; Jahns, Steven K.; Brown, T.; McKinney, B. (United States. Federal Highway Administration, 1993-10)
    This report is one of a series produced as part of a contract designed to develop precise, detailed, human factors design guidelines for Advanced Traveler Information Systems (ATIS) and Commercial Vehicle Operations (CVO). The goals of the work covered in this report were to: (1) identify information format alternatives for ATIS devices for both private drivers and CVO applications, and (2) identify research issues that must be addressed in order to develop effective information format guidelines. To achieve these goals, and to make the greatest progress possible toward the ultimate project goal of guideline development, the project developed the strategy of turning the current state of knowledge into tools applicable to any ATIS design. Four primary design-decision tools were developed. These tools are intended to help either professional or nonprofessional human factors designers make appropriate tradeoff decisions in designing effective ATIS displays. The four tools are: (1) Sensory Modality Allocation, (2) Trip Status Allocation, (3) Display Format Allocation, and (4) Display Location.
  • Thumbnail Image
    E-Scooter Design: Safety Measures for Next Gen Scooter
    Novotny, Adam; Mollenhauer, Michael A.; White, Elizabeth (Safe-D National UTC, 2023-05)
    Over the recent years, e-scooters have become an increasingly popular and convenient micromobility solution for short-distance trips for a wide demographic of users. Due to their accessibility, knowledge regarding proper e-scooter use and level of operating experience can vary widely. With the increase in use, there has been a rise in injuries for e-scooter riders and other road users. One possible cause is that the true performance capabilities of e-scooters vary based upon their designs; users are unaware of these differences or how to accommodate their riding behavior to retain a safe experience. This relationship between safety outcomes and e-scooter design attribute has yet to be established. Until recently, very little formal research has been conducted on the safety of this form of transportation or on the optimal design for e-scooters. Safety concerns may limit the widespread adoption of e-scooters as a legitimate transportation option. To address this concern, the Virginia Tech Transportation Institute (VTTI), in collaboration with Ford Motor Company and Spin, conducted a controlled participant study on the Virginia Smart Roads to evaluate and compare various e-scooter designs and study how rider specific factors contribute to performance and safety. The results from this study will be used to inform e-scooter companies and manufacturers on design recommendations for improved e-scooter safety.
  • Thumbnail Image
    E-Scooter Safety Assessment and Campus Deployment Planning
    White, Elizabeth; Mollenhauer, Michael A.; Robinson, Sarah; Novotny, Adam (Safe-D University Transportation Center, 2023-12)
    E-Scooters are a popular new service that provide last mile transportation, but there are reports of safety concerns for riders and impingement on other users of rights of way. Little formal research has been conducted on E-Scooter safety or the optimal approach to deployment to decrease nuisance issues. To address this, VTTI and Spin deployed a fleet of E-Scooters on the Virginia Tech campus through an exclusive, controlled research program. Through on-scooter data acquisition systems, fixed infrastructure cameras, anecdotal injury reports, and surveys, data was collected to assess safety impact as well as to understand beneficial and problematic user behaviors and patterns for subsequent countermeasure development and deployment recommendations. The resulting naturalistic dataset includes over 9,000 miles of riding data. Overall, the E-Scooter deployment on the Virginia Tech campus was safer than other reported deployments. The operational constraints that were put in place were largely effective, and with the additional results from this study, some additional constraints and expanded outreach programs may make future deployments even safer. The campus community largely considered the deployment of E-Scooters a clean alternative transportation option and viewed the service favorably.
  • Thumbnail Image
    Effectiveness of Vehicle External Communication Toward Improving Vulnerable Road User Safe Behaviors: Considerations for Legacy Vehicles to Automated Vehicles of the Future
    Rossi-Alvarez, Alexandria Ida (Virginia Tech, 2023-01-25)
    Automated vehicles (AVs) will be integrated into our society at some point in the future, but when is still up for debate. An extensive amount of research is being completed to understand the communication methods between AVs and other road users sharing the environment to prepare for this future. Currently, researchers are working to understand how different forms of external communication on the AVs will impact vulnerable road user (VRU) interaction. However, within the last 10 years, VRU casualty rates have continued to rise for all classifications of VRUs. Unfortunately, there is no suggestion that pedestrian fatality rates will ever decrease without some intervention. This dissertation aims at understanding the impacts of eHMI across real-world, complex scenarios with AVs and how researchers can apply those future findings to improve VRUs' judgments to today. A series of studies evaluated the necessity and impact of eHMI on AV–VRU interaction, assessed how the visual components of eHMI influenced VRU crossing decisions, and how variations in a real-world environment (multiple vehicles and scenario complexity) impact crossing decision behavior. Two studies examined how eHMI will impact future interactions between AVs and VRUs. Specifically, to understand how to advance the design of these future devices to avoid unintended consequences that may result. Results from these studies found that the presence and condition of eHMI did not influence participants' willingness to cross. Participants primarily relied on the speed and distance of the vehicle to make their crossing decision. It was difficult for participants to focus on the eHMI when multiple vehicles competed for their attention. Participants typically prioritized their focus on the vehicle that was nearest and most detrimental to their crossing path. Additionally, the type of scenario caused participants to make more cautious crossing decisions. However, it did not influence their willingness to cross. The last study applied the learnings from the first two studies to a foundational perception study for current legacy vehicles. These results showed a significant increase in judgment accuracies with a display. Through analysis across overall conclusions from the 3 studies, five critical findings were identified when addressing eHMI and 3 design recommendations, which are discussed in the penultimate section of this work. The results of this dissertation indicate that eHMI improved VRUs' accuracy of perception of change in vehicle speed. eHMI did not significantly impact VRUs crossing decisions. However, the complexity of the traffic scenarios affected the level of caution participants exhibited in their crossing behavior.
  • Thumbnail Image
    Evaluation Tools for Low-Speed Automated Vehicle (LSAV) Transit Readiness of the Area
    Hong, Yubin; Klauer, Charlie; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul; Goodall, Noah; Fontaine, Michael D. (Safe-D National UTC, 2022-11-22)
    Automated shuttles are small, low-speed (generally less than 25 mph) vehicles that do not require a human operator, though to date all have included an onboard human attendant. This project aims to assess the limitations that the EasyMile EZ10 Gen 3 low-speed automated vehicle (LSAV) encountered while operating on public roadways. The primary interests are to evaluate the infrastructure elements that posed the most challenges for the LSAV during its deployment. Further, the EasyMile EZ10 Gen 3 is advertised as being capable of operating at SAE International Level 4 Automated Driving System capability in certain ODDs. Accordingly, the team deployed the LSAV with the expectation that it would be operated at SAE Level 2 capability. The human safety operator was required to intervene in scenarios beyond the vehicle’s automated functional capability. The results of this analysis indicated that the LSAV operated at a lower than expected speed, experienced a high frequency of disengagements, and had a regular need for safety operator intervention. These results suggest that the EZ10 Gen 3 vehicle is not yet operating at SAE International Level 4 capability on routes with moderate complexity.
  • Thumbnail Image
    Fiber Sensing: Real-time, Long-distance Traffic Monitoring
    Viray, Reg; Mollenhauer, Michael A.; Chen, Yuheng; Huang, Ming-Fang (2024-07-26)
    The report presents the pilot testing of NEC’s distributed fiber optic sensing (DFOS) and artificial intelligence behavior detection technologies on the Virginia Smart Roads. The intent was to explore the viability of DFOS through existing fiber installations for traffic management and road safety, potentially superseding traditional methods like cameras or radar. Key use cases for DFOS include identifying wrong-way driving, traffic queues, emergency stops, and other non-standard driving behaviors, which are all critical for traffic management and safety. NEC collaborated with the Virginia Tech Transportation Institute to install fiber optics along a highway section conforming to Virginia Department of Transportation (VDOT) standards and executed orchestrated driving scenarios to test detection capabilities under various traffic and weather conditions. The results of these tests demonstrated that DFOS could effectively detect wrong-way driving across different traffic densities. However, challenges remain with vehicle count and speed accuracy in high-traffic situations. In addition, the DFOS hardware solutions would need to be deployable in roadside cabinets with limited environmental control to be viable for deployment across VDOT’s roadway system. The project underscores the strategic advantage of using existing network infrastructures for monitoring and suggests that while the NEC system shows promise in certain applications, further refinements are needed for handling complex traffic scenarios.
  • Thumbnail Image
  • Thumbnail Image
    Information Freshness: How To Achieve It and Its Impact On Low- Latency Autonomous Systems
    Choudhury, Biplav (Virginia Tech, 2022-06-03)
    In the context of wireless communications, low latency autonomous systems continue to grow in importance. Some applications of autonomous systems where low latency communication is essential are (i) vehicular network's safety performance depends on how recently the vehicles are updated on their neighboring vehicle's locations, (ii) updates from IoT devices need to be aggregated appropriately at the monitoring station before the information gets stale to extract temporal and spatial information from it, and (iii) sensors and controllers in a smart grid need to track the most recent state of the system to tune system parameters dynamically, etc. Each of the above-mentioned applications differs based on the connectivity between the source and the destination. First, vehicular networks involve a broadcast network where each of the vehicles broadcasts its packets to all the other vehicles. Secondly, in the case of UAV-assisted IoT networks, packets generated at multiple IoT devices are transmitted to a final destination via relays. Finally for the smart grid and generally for distributed systems, each source can have varying and unique destinations. Therefore in terms of connectivity, they can be categorized into one-to-all, all-to-one, and variable relationship between the number of sources and destinations. Additionally, some of the other major differences between the applications are the impact of mobility, the importance of a reduced AoI, centralized vs distributed manner of measuring AoI, etc. Thus the wide variety of application requirements makes it challenging to develop scheduling schemes that universally address minimizing the AoI. All these applications involve generating time-stamped status updates at a source which are then transmitted to their destination over a wireless medium. The timely reception of these updates at the destination decides the operating state of the system. This is because the fresher the information at the destination, the better its awareness of the system state for making better control decisions. This freshness of information is not the same as maximizing the throughput or minimizing the delay. While ideally throughput can be maximized by sending data as fast as possible, this may saturate the receiver resulting in queuing, contention, and other delays. On the other hand, these delays can be minimized by sending updates slowly, but this may cause high inter-arrival times. Therefore, a new metric called the Age of Information (AoI) has been proposed to measure the freshness of information that can account for many facets that influence data availability. In simple terms, AoI is measured at the destination as the time elapsed since the generation time of the most recently received update. Therefore AoI is able to incorporate both the delay and the inter-packet arrival time. This makes it a much better metric to measure end-to-end latency, and hence characterize the performance of such time-sensitive systems. These basic characteristics of AoI are explained in detail in Chapter 1. Overall, the main contribution of this dissertation is developing scheduling and resource allocation schemes targeted at improving the AoI of various autonomous systems having different types of connectivity, namely vehicular networks, UAV-assisted IoT networks, and smart grids, and then characterizing and quantifying the benefits of a reduced AoI from the application perspective. In the first contribution, we look into minimizing AoI for the case of broadcast networks having one-to-all connectivity between the source and destination devices by considering the case of vehicular networks. While vehicular networks have been studied in terms of AoI minimization, the impact of mobility and the benefit of a reduced AoI from the application perspective has not been investigated. The mobility of the vehicles is realistically modeled using the Simulation of Urban Mobility (SUMO) software to account for overtaking, lane changes, etc. We propose a safety metric that indicates the collision risk of a vehicle and do a simulation-based study on the ns3 simulator to study its relation to AoI. We see that the broadcast rate in a Dedicated Short Range Network (DSRC) that minimizes the system AoI also has the least collision risk, therefore signifying that reducing AoI improves the on-road safety of the vehicles. However, we also show that this relationship is not universally true and the mobility of the vehicles becomes a crucial aspect. Therefore, we propose a new metric called the Trackability-aware AoI (TAoI) which ensures that vehicles with unpredictable mobility broadcast at a faster rate while vehicles that are predicable are broadcasting at a reduced rate. The results obtained show that minimizing TAoI provides much better on-road safety as compared to plain AoI minimizing, which points to the importance of mobility in such applications. In the second contribution, we focus on networks with all-to-one connectivity where packets from multiple sources are transmitted to a single destination by taking an example of IoT networks. Here multiple IoT devices measure a physical phenomenon and transmit these measurements to a central base station (BS). However, under certain scenarios, the BS and IoT devices are unable to communicate directly and this necessitates the use of UAVs as relays. This creates a two-hop scenario that has not been studied for AoI minimization in UAV networks. In the first hop, the packets have to be sampled from the IoT devices to the UAV and then updated from the UAVs to the BS in the second hop. Such networks are called UAV-assisted IoT networks. We show that under ideal conditions with a generate-at-will traffic generation model and lossless wireless channels, the Maximal Age Difference (MAD) scheduler is the optimal AoI minimizing scheduler. When the ideal conditions are not applicable and more practical conditions are considered, a reinforcement learning (RL) based scheduler is desirable that can account for packet generation patterns and channel qualities. Therefore we propose to use a Deep-Q-Network (DQN)-based scheduler and it outperforms MAD and all other schedulers under general conditions. However, the DQN-based scheduler suffers from scalability issues in large networks. Therefore, another type of RL algorithm called Proximal Policy Optimization (PPO) is proposed to be used for larger networks. Additionally, the PPO-based scheduler can account for changes in the network conditions which the DQN-based scheduler was not able to do. This ensures the trained model can be deployed in environments that might be different than the trained environment. In the final contribution, AoI is studied in networks with varying connectivity between the source and destination devices. A typical example of such a distributed network is the smart grid where multiple devices exchange state information to ensure the grid operates in a stable state. To investigate AoI minimization and its impact on the smart grid, a co-simulation platform is designed where the 5G network is modeled in Python and the smart grid is modeled in PSCAD/MATLAB. In the first part of the study, the suitability of 5G in supporting smart grid operations is investigated. Based on the encouraging results that 5G can support a smart grid, we focus on the schedulers at the 5G RAN to minimize the AoI. It is seen that the AoI-based schedulers provide much better stability compared to traditional 5G schedulers like the proportional fairness and round-robin. However, the MAD scheduler which has been shown to be optimal for a variety of scenarios is no longer optimal as it cannot account for the connectivity among the devices. Additionally, distributed networks with heterogeneous sources will, in addition to the varying connectivity, have different sized packets requiring a different number of resource blocks (RB) to transmit, packet generation patterns, channel conditions, etc. This motivates an RL-based approach. Hence we propose a DQN-based scheduler that can take these factors into account and results show that the DQN-based scheduler outperforms all other schedulers in all considered conditions.
  • Thumbnail Image
    Infrastructure-Based Performance Evaluation for Low-Speed Automated Vehicle (LSAV)
    Klauer, Charlie; Hong, Yubin; Mollenhauer, Michael A.; Vilela, Jean Paul Talledo (MDPI, 2023-05-05)
    This study assessed the limitations of the EasyMile EZ10 Gen 3 low-speed automated vehicle (LSAV) while operating on public roadways. The primary interest was to evaluate the infrastructure elements that posed the greatest challenges for the LSAV. A route was chosen that would satisfy a legitimate transit need. This route included more operational complexity and higher traffic volumes than a typical EasyMile LSAV deployment. The results indicate that the LSAV operated at a lower-than-expected speed (6 to 8 mph), with a high frequency of disengagements, and a regular need for safety operator intervention. Four-way stop-sign controlled intersections, three-lane roads with a shared turning lane in the middle, open areas, and areas without clear markings were the most challenging for the LSAV. Some important considerations include the need to have LSAVs operate on roadways where other vehicles may pass more safely, or on streets with slower posted speed limits. Additionally, the low passenger capacity and inability to understand where passengers are located onboard make it hard for the LSAV to replace bus transits. Currently, the LSAV is best suited to provide first/last-mile services, short routes within a controlled access area, and fill in gaps in conventional transits.
  • Thumbnail Image
    Introduction to Communications in Transportation
    Mollenhauer, Michael A.; Robinson, Sarah; Vilela, Jean Paul Talledo; Vaughn, Will (Safe-D National UTC, 2022-10)
    As new Intelligent Transportation Systems (ITS) and vehicle-to-everything (V2X) communication technology and protocols continue to emerge, additional training is needed for personnel working in the transportation sector. The Virginia Department of Transportation has already created a training program focusing on general topics pertaining to connected and automated vehicles (CAVs) and has recently identified a need for a more specific program focusing on communication technologies. To address this need, the Virginia Tech Transportation Institute team developed a 60-minute online learning program that includes a series of 10 narrated modules with slides, images, charts, videos, and learning assessments. The training provides a high-level overview of the types of communications that support ITS, traffic management, and connected vehicle environments. The training includes descriptions of the communication technologies, protocols, performance metrics, use cases, and data security. The included communication technologies are currently being utilized by infrastructure owner-operators (IOOs), original equipment manufacturers (OEMs), and industry technology providers.
  • Thumbnail Image
    Mobile User Interface Development for the Virginia Connected Corridors
    Mollenhauer, Michael A.; Noble, Alexandria M.; Doerzaph, Zachary R. (Connected Vehicle/Infrastructure University Transportation Center, 2016-10-15)
    The purpose of this research and development activity was to build a mobile application with a low-distraction user interface appropriate for use in a connected vehicle (CV) environment. To realize their full potential, future CV applications will involve communicating information to and from drivers during vehicle operation. Mobile devices such as smart phones and tablets may be a reasonable hardware platform to provide this communication. However, there are concerns that a potential increase in driver interaction with CV applications may lead to driver distraction and possible negative impacts on driving safety. The prototype mobile device user interface that was designed and created during this project can be used to test new CV applications, validate their impact on driver safety, and inform future mobile device user interface standards for driving applications.
  • Thumbnail Image
    Private 5G Technology and Implementation Testing
    Vilela, Jean Paul Talledo; Mollenhauer, Michael A.; White, Elizabeth E.; Miller, Marty (Safe-D National UTC, 2023-03)
    NEC developed a Video Analytics implementation for traffic intersections using 5G technology. This implementation included both hardware infrastructure and software applications supporting 5G communications, which allows low latency and secure communications. The Virginia Tech Transportation Institute (VTTI) worked with NEC to facilitate the usage of a 3,400- to 3,500-MHz program experimental license band without SAS integration to successfully implement a private 5G deployment at the VTTI Smart Road intersection and data center. Specific use cases were developed to provide alerting mechanisms to both pedestrians and vehicles using cellular vehicle-to-everything/PC5 technology when approaching a traffic intersection and a dangerous situation is detected.
  • Thumbnail Image
    Signal Awareness Applications
    Mollenhauer, Michael A.; Viray, Reginald; Doerzaph, Zachary R.; White, Elizabeth; Song, Miao (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-09)
    Intersection collisions account for 40% of all crashes on U.S. roadways. It is estimated that 165,000 accidents, which result in approximately 800 fatalities annually, are due to vehicles that pass through intersections during red signal phases. Although infrastructure-based red-light violation countermeasures have been deployed, intersections remain a top location for vehicle crashes. The Virginia Department of Transportation and its research arm, the Virginia Transportation Research Council, partnered with the Virginia Tech Transportation Institute to create the Virginia Connected Corridors (VCC), a connected vehicle test bed located in Fairfax and Blacksburg, Virginia, that enables the development and assessment of early-stage connected and automated vehicle applications. Recently, new systems have been deployed that transmit position correction messages to support lane-level accuracy, enabling development of signal awareness applications such as red-light violation warning. This project enhances the current capabilities of VCC platforms by developing new signal awareness safety and mobility features. Additionally, this project investigated the technical and human factors constraints associated with user interfaces for notifying and alerting drivers to pertinent intersection-related information to curb unsafe driving behaviors at signalized intersections.
  • Thumbnail Image
    Smart Work Zone System
    Talledo Vilela, Jean Paul; Mollenhauer, Michael A.; White, Elizabeth E.; Vaughan, Elijah W.; Burdisso, Daniel (Safe-D National UTC, 2022-10)
    In the previous Safe-D project 04-104, a prototype wearable Personal Protective Equipment vest that accurately localizes, monitors, and predicts potential collisions between work zone (WZ) workers and passing motorists was developed and demonstrated. The system also notifies the worker when they’re about to depart geo-fenced safe areas within WZs. While the design supported a successful functional demonstration, additional design iteration was required to simplify, ruggedize, and reduce per unit costs to increase the likelihood of broader adoption. In addition, two new useful components were identified that support a more effective deployment package. One of these components is a Base Station that provides an edge computing environment for alert algorithm processing, consolidates communications of individual worker positions via a 4G link to a cloud computing environment, and can be coupled with a local roadside unit to support the broadcast of WZ information to connected and automated vehicles. The second component is a Smart Cone device that was added to help automatically define safe area boundaries and improve communications reliability between workers and the Base Station. This entire package was developed to support a broader scale deployment of the technology by the Virginia Department of Transportation.