Work Zone Automation (ATMA)
Driverless follow trucks that take highway maintenance workers out of the crash zone.
Why it matters
Mobile and slow-moving maintenance operations, such as striping, sweeping, bridge flushing, and pothole patching, put workers in slow trucks on high-speed roads. In 2017, U.S. work zones saw 158,000 crashes and 61,000 injuries, many involving state DOT employees. In Missouri alone, slow-moving operations had been struck more than 80 times since 2013.
What we do
An Autonomous Truck Mounted Attenuator (ATMA) is a leader-follower cooperative driving automation system. A human-driven lead truck performs the work. A driverless follow truck tracks the lead truck over V2V communication and carries a truck mounted attenuator, so that if a rear-end crash is unavoidable, no one is in the truck that takes the hit.
Our lab pioneered research on this system and has supported it from field testing to deployment. We work with USDOT and FHWA, Colorado DOT, Missouri DOT, and the Autonomous Maintenance Technology pooled fund, whose members include 17 state DOTs. State DOTs including Colorado and Missouri have deployed the technology.
Under a USDOT SMART grant, we are working with five state DOTs (Colorado, Minnesota, Wisconsin, Oklahoma, and California) to deploy ATMA across diverse operating environments, and we have published the resulting field data with a citable DOI.
Outreach
Dr. Hu organized the TRB webinar “Saving Lives with Autonomous Truck Mounted Attenuator Systems” (June 2022), with speakers from a state DOT, industry, and academia. It drew nearly 300 attendees.
What we work on
Field testing and evaluation
Test protocols and statistical methods that turn field logs into defensible performance conclusions. With Missouri DOT we designed 31 test scenarios covering communication loss, following accuracy, obstacle detection, and emergency situations.
- Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance (TRR, 2021)
Operational design domain and guidelines
Where and when an ATMA can operate safely. We model merging traffic around the work convoy on two-lane and multilane highways to identify the operational design domain, and translate the results into operation and deployment guidelines for state DOTs, including an ATMA toolkit for USDOT and deployment guidelines for Colorado DOT (2025).
- Autonomous Truck Mounted Attenuator (ATMA) Toolkit (USDOT report, 2025)
- Development of Autonomous Truck Mounted Attenuator (ATMA) Deployment Guidelines Considering Traffic and Safety Impacts (CDOT report, 2025)
- Identification of Operational Design Domain for Autonomous Truck Mounted Attenuator System on Multilane Highways (TRR, 2022)
Resilience and localization
Keeping the follow truck on the lead truck's path when GPS degrades: LiDAR-based cooperative localization and, with Royal Truck, a LiDAR SLAM algorithm for the leader-follower system.
- Lightweight LiDAR-Based Cooperative Localization Model for Asymmetric Leader-Follower Cooperative Driving Automation System (IEEE T-ITS, 2026)
Operations planning
Planning maintenance work around the capabilities of leader-follower vehicles, including route optimization for highway maintenance.
- Optimizing Maintenance Routes for Highway Infrastructure Using Leader-Follower Autonomous Vehicles (Automotive Innovation, 2026)
Representative work
Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance
A field-testing program with Missouri DOT (31 scenarios, each repeated three times) and a three-layer statistical framework. The follow truck stayed within ±6 inches of the lead truck under most conditions and stopped within 1.9 seconds when both V2V radios failed.
Autonomous Truck Mounted Attenuator (ATMA) Toolkit
A practitioner toolkit for agencies planning and operating ATMA deployments.
Identification of Operational Design Domain for Autonomous Truck Mounted Attenuator System on Multilane Highways
Identifies the operational design domain in which an ATMA system can run safely on multilane highways, giving agencies a basis for deciding where and when to deploy it.
Lightweight LiDAR-Based Cooperative Localization Model for Asymmetric Leader-Follower Cooperative Driving Automation System
A lightweight LiDAR-based model that lets the follower vehicle localize itself relative to the leader in an asymmetric leader-follower system, reducing reliance on GPS.
Optimizing Maintenance Routes for Highway Infrastructure Using Leader-Follower Autonomous Vehicles
Optimizes maintenance routes when the work is performed by leader-follower autonomous vehicles, so agencies can plan ATMA operations across a highway network.
Videos
Colorado DOT unveils the world's first self-driving work zone vehicle
ABC News: Self-driving trucks, automation that can save lives on the road
Missouri DOT field testing and evaluation with Missouri S&T
Leader-follower cooperative driving automation simulation
Sponsored projects
All projectsATMA for Work Zone Safety
U.S. Department of Transportation (SMART Program)
2024–2026
PSU PIDevelopment of ATMA/AIPV Deployment Guidelines Considering Traffic and Safety Impacts
Autonomous Maintenance Technology Pooled Fund, Colorado DOT
2023–2025
PIEnhance Autonomous Truck-Mounted Attenuator System Resiliency in a GPS-Denied Environment
Pennsylvania Department of Community and Economic Development
2023–2024
PIField Evaluation of Autonomous Truck Mounted Attenuator Vehicles During Live Work Zone Operations
Kratos Defense
2022
PISystematic Documentation for Autonomous Maintenance Technology Promotion and Deployment
Autonomous Maintenance Technology Pooled Fund, Colorado DOT
2020–2026
PISystematic Documentation for Autonomous Maintenance Technology Promotion and Deployment
Colorado Department of Transportation
2020–2022
PIOptimization of Transportation Infrastructure System Performance with Autonomous Maintenance Technology in Work Zones
U.S. Department of Transportation (Mid-America Transportation Center)
2020–2021
PIAnalyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management
U.S. Department of Transportation (NCTriDurLE UTC)
2020–2022
PSU PIDevelopment of ATMA/AIPV Deployment Guidelines Considering Traffic and Safety Impacts
Colorado Department of Transportation, U.S. Department of Transportation (Mid-America Transportation Center)
2019–2022
PIEvaluation and Behavioral Interventions for Safe Deployment of Autonomous Truck Mounted Attenuator Vehicles: A Driving Simulator Based Study
Intelligent Systems Center, Missouri S&T
2019–2020
Co-PIAnalyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management
U.S. Department of Transportation (NCTriDurLE UTC)
2019–2021
PIDevelopment of Autonomous Trucks Operation Guidelines and Driver Training Process
U.S. Department of Transportation (Mid-America Transportation Center)
2018–2020
PIMissouri DOT Leader-Follower TMA System
Missouri Department of Transportation
2018–2020
Co-PI (Missouri S&T PI)
Recent publications
All 8 in this thrustLightweight LiDAR-Based Cooperative Localization Model for Asymmetric Leader-Follower Cooperative Driving Automation System
Optimizing Maintenance Routes for Highway Infrastructure Using Leader-Follower Autonomous Vehicles
Development of Autonomous Truck Mounted Attenuator (ATMA) Deployment Guidelines Considering Traffic and Safety Impacts
Autonomous Truck Mounted Attenuator (ATMA) Toolkit
A Multi-State Merging Based Analytical Model for an Operation Design Domain of Autonomous Vehicles in Work Zones on Two-lane Highways
Identification of Operational Design Domain for Autonomous Truck Mounted Attenuator System on Multilane Highways
Development of Operation Guidelines for Leader-Follower Autonomous Maintenance Vehicles at Work Zone Locations
Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance
Work with us
State DOTs considering ATMA can use our test protocols, operational design domain analysis, and deployment guidelines. We support agencies from procurement evaluation through field deployment.
Email Dr. Hu