Abstract
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 a 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), 2 despite a decrease in the number of active construction sites between 2013 and 2014. Although various efforts have been made to improve TMA driver crashworthiness (e.g., by adding interior padding, harnesses, and supplemental head restraints),1 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. 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 vehicle.
Project Highlights
- The second phase of VTTI’s ATMA successfully integrated a machine-vision based navigation fallback.
- Automated following was demonstrated up to 40 mph and operated for durations exceeding 3 hours of continuous automation including lengthy periods of remaining stationary.
- Robustness and reliability were greatly improved while reducing the total cost and hardware footprint.
- The ATMA lead follow system successfully performed a mobile right-lane closure of US 460-E between Tom’s Creek Rd and Industrial Park Drive while in automation, passing underneath 3 overpasses, switching between vision and GPS-based navigation.
Final Report
EWD & T2 Products
ROS bags from live road test (a collection of sensor and operational data from the ATMA and lead vehicle):
Live Road Test Event: A right light mobile closure of US-460 between Industrial Park Dr and Toms Creek Rd in a VDOT WAPM 11.2 configuration operated by a real VDOT crew. Videos of Live Testing Event are available here.
Invention Disclosure
Functioning ATMA System able to operate in GPS denied environment
Product Marketing sheet (1-pager)
Student Impact Statement(pdf): Two Undergraduate Virginia Tech students received funding under this project (Johann Hamen, BS Mechanical Engineering and Daniel Burdisso, BS Computer Science). This file contains a statement by these students as to the impact this project had on education and workforce development.
Presentations/Publications
Advanced Maintenance Technologies Pooled Fund Annual Conference, Oct 4 2023, 20 minute presentation, 1 hour live demo, 20 state DoT CAV leaders, in person at VTTI and online
DRIVE Smart Distracted Driving Summit, October 2023,4 hours, 250 attendees including media, in person at VTTI, https://articles.vafb.com/news-and-features/categories/innovators-improve-work-zone-safety-through-automation-at-vt-transportation-institute
VDOT Roadeo, July 28 2022, 300-400 attendees, live demo, Doswell Fair grounds
VDOT Operations Workshop, 200 attendees, live demo, Richmond central office
Lexington Live Road Pilot, Oct 17-18, 7 hours total, live pilot on I-64, Lexington
DCS Transportation Infrastructure Maintenance Symposium, Aug 17 2023, 150 attendees, static demo, Short Pump
Dock to Door, May 4 2023, 80 attendees, live demo, VTTI Rural road
Ut Prosim Donor Event, April 29 2023, 60 attendees, presentation, VTTI
Final Dataset
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/A1FNMW.
Research Investigators (PI*)
Mike Mollenhauer (VTTI/VT)*
Elizabeth White (VTTI/VT)*
Jean Paul Talledo Vilela (VTTI/VT)*
Will Vaughan (VT)*
Project Information
Start Date: 2022-01-01
End Date: 2023-09-30
Status: Completed
Grant Number: 69A3551747115
Total Funding: $274,974
Source Organization: Safe-D National UTC
Project Number: 06-005
Safe-D Theme Areas
Automated Vehicles
Connected Vehicles
Safe-D Application Areas
Risk Assessment
Vulnerable Users
Driver Factors and Interfaces
More Information
Sponsor Organization
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Performing Organization
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA