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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.

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.

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.

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.

This report describes testing equipment and procedures developed by the Center for Technology Development at the Virginia Tech Transportation Institute (VTTI) to evaluate critical attributes for cameras used in naturalistic driving research. A survey of VTTI researchers was conducted to determine the most important attributes and known issues from past naturalistic driving studies. The data collected were used to design and build a camera testing apparatus and a field of view (FOV) testing apparatus that allow the standardized testing of a number of important attributes, including image clarity, black and white versus color display, FOV, and the quality of the image under various lighting conditions. The camera testing apparatus also has the ability to detect infrared (IR) sources in cameras. In addition, this report includes a set of recommended methods for testing system resolution using the International Organization for Standardization (ISO) 12233 standard test target and image sharpness using free software or commercial software that would provide increased accuracy and decreased training times. Procedures are also described for testing environmental factors such as temperature range, temperature cycling, and immersion.