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Artificial Intelligence Deployment Center of Excellence (AI COE)

rosaP Repository & Open Science Access Portal

The AI COE Collection is a central resource for transportation stakeholders that may aid in deployment of safe and responsible AI in surface transportation. The collection will feature current and emerging resources, innovations, and technologies. For more information on the AI COE, go to https://highways.dot.gov/ai-coe.

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Showing 181 - 200 of 613 results
  • The Good the Bold and the Useful Updates on Activities and Initiatives within the World of Transportation Libraries
    The Good, the Bold, and the Useful: Updates on Activities and Initiatives within the World of Transportation Libraries
    Molina, Michael ;
    White, Enid
    2024-09-12 | MP4
    This session of the Transportation Librarians Roundtable marks a return to the group updates format. Michael Molina discusses the origins of the Pooled Fund Study, current work as it gets ready to wind down in 2025, and outcomes from the project. With 18 members from states across the U.S., the study resulted in digitization of thousands of materia ...
    Supporting Files
  • How Effective are Marker Variables at Predicting Attitudinal Factor Scores An Outofsample Evaluation
    How Effective are Marker Variables at Predicting Attitudinal Factor Scores? An Out-of-sample Evaluation
    Soria, Jason ;
    Mokhtarian, Patricia L.
    2024-09-01 | PDF
    This report is one in a series of studies designed to investigate the practicality of including attitudes as explanatory variables in practice-oriented travel demand forecasting models. In this study, we respectively applied random forest (RF) and elastic net regression (ENR) to 15 marker variables (MVs), to predict factor scores on four attitudes: ...
  • Deep LearningBased Computer Vision Framework for Predicting Retroreflectivity of Road Signs
    Deep Learning-Based Computer Vision Framework for Predicting Retroreflectivity of Road Signs
    Ruganuza, Denis ;
    Sulle, Methuselah
    2024-09-01 | PDF
    The retro-reflectivity of traffic signs is essential for road safety, particularly in low-light and adverse weather conditions, by ensuring signs remain visible and legible to road users. However, retro-reflective quality degrades over time due to environmental exposure, reducing sign effectiveness and increasing risks of road accidents and potenti ...
  • Development of MultiRotorUAVBased Rail Track Irregularity Monitoring and Measuring Platform with Image and Lidar Sensors
    Development of Multi-Rotor-UAV-Based Rail Track Irregularity Monitoring and Measuring Platform with Image and Lidar Sensors
    Qiu, Lihao ;
    Zhu, Ming
    2024-09-01 | PDF
    This project aimed to develop a multi-rotor UAV-based track geometry measurement system using image and LiDAR sensors that does not require the closure of track during inspection. The research was divided into three stages: exploration of optimal path planning, development of a LiDAR-only track geometry measurement system, and development of a came ...
  • ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models [IEEE ITSC 2024] [supporting dataset]
    Rajkumar, Ragunathan (Raj) ;
    Sahu, Nishad
    2024-09-01 | ZIP
    In this work, we introduce a dataset called DrivingContexts for the detection of relevant driving contexts for autonomous driving. Additionally, we propose the use of vision language models such as LLaVa and ViLT with zero-shot and few-shot approaches, to solve the problem of detecting such contexts. With our approach, we reduce the need for fully ...
    Dataset
  • Data-Driven Combined Longitudinal and Lateral Control for the Car Following Problem [supporting dataset]
    Li, Xianning
    ORCID ICON IMG
    2024-09-01 | ZIP
    Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions ...
    Dataset Supporting Files
  • Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic A LearningBased Approach
    Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach
    Jiang, Zhong-Ping ;
    Ozbay, Kaan
    2024-09-01 | PDF
    Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions ...
  • Building Digital Twins via GPU-Accelerated Human-Regularized Reinforcement Learning [supporting dataset]
    Vinitsky, Eugene ;
    Cornelisse, Daphne
    2024-09-01 | ZIP
    We have carried out a two-component study investigating how to build high-performing human driver models. First, we investigated how to combine large-scale datasets from autonomous vehicle companies with reinforcement learning methods for training agents. We showed that by adding a supervised learning loss atop the model training, we could build mo ...
    Dataset Supporting Files
  • Building Digital Twins via GPUAccelerated HumanRegularized Reinforcement Learning
    Building Digital Twins via GPU-Accelerated Human-Regularized Reinforcement Learning
    Vinitsky, Eugene ;
    Cornelisse, Daphne
    2024-09-01 | PDF
    We have carried out a two-component study investigating how to build high-performing human driver models. First, we investigated how to combine large-scale datasets from autonomous vehicle companies with reinforcement learning methods for training agents. We showed that by adding a supervised learning loss atop the model training, we could build mo ...
  • EarthquakeInduced Damage Classification of Bridges Using Artificial Neural Network
    Earthquake-Induced Damage Classification of Bridges Using Artificial Neural Network
    Chen, Genda ;
    Alomari, Ibrahim
    2024-09-01 | PDF
    Ensuring the resilience of bridges against earthquakes is crucial for the rapid recovery of communities. Speedy and accurate damage assessments are essential for pinpointing the most affected areas and facilitating prompt decision-making. Current methods (i.e., fragility curves and estimates) are often slow and inaccurate. To address these issues, ...
  • Functional CompositeBased Wireless Sensing Platform for Bridge Structures
    Functional Composite-Based Wireless Sensing Platform for Bridge Structures
    Wu, Chenglin ;
    Li, Yanxiao
    2024-09-01 | PDF
    The main objective of this research is to develop and deploy a wireless crack sensing system that can measure and monitor cracks for both concrete and steel bridge structures. This system will contain the sensing unit, wireless data transmitting system, as well as a data processing unit. The sensing unit will consist of single or arrays of advanced ...
  • Artificial Intelligence for Pedestrian and Bicyclist Safety Using AI To Detect NearMiss Collisions Research Brief
    Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI To Detect Near-Miss Collisions [Research Brief]
    Pourhomayoun, Mohammad
    ORCID ICON IMG
    2024-09-01 | PDF
    For this project, we developed an end-to-end system based on advanced artificial intelligence (AI) models and effective computer vision algorithms to detect and report near-miss collisions as an important indicator to identify and measure safety risks, especially in specific circumstances such as right turn on a red light. The main objective is to ...
  • Investigation of Driver Adaptations in a Mixed Traffic Environment
    Investigation of Driver Adaptations in a Mixed Traffic Environment
    Kondyli, Alexandra ;
    Lane, Bradley W
    2024-09-01 | PDF
    This study investigates the impact of semi-automated vehicle (SAV) systems, specifically Adaptive Cruise Control (ACC), on driver behavior and control transitions (CT). The variations and adaptations in driver performance due to mental workload, influenced by factors such as task difficulty and driver awareness, are critical, especially under diffe ...
  • Understanding AI Risks in Transportation AI Assurance for Transportation Whitepaper Series
    Understanding AI Risks in Transportation: AI Assurance for Transportation Whitepaper Series
    United States. Department of Transportation. Highly Automated Systems Safety Center of Excellence
    2024-09-01 | PDF
    Our first AI Assurance (AIA) whitepaper, An Overview of AI Assurance for Transportation, was published in April 2024. One of the identified key areas for AIA research is AI risk assessment and mitigation, which has not been explored sufficiently in the past. To promote and accelerate research efforts to address AI risks, this whitepaper-as the seco ...
  • Transit Signal Priority Control With Connected Vehicle Technology Deep Reinforcement Learning Approach
    Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach
    Fan, Wei ;
    Yang, Tianjia
    2024-09-01 | PDF
    Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service. However, this control strategy generally causes adverse effects on other traffic, which limits its widespread adoption. The development of Connected Vehicle (CV) technology enables the real-time acquisit ...
  • Measuring the Impact of Roadside Features on RoadDeparture Crashes and Prioritizing Safety Improvement Projects
    Measuring the Impact of Roadside Features on Road-Departure Crashes and Prioritizing Safety Improvement Projects
    Subedi, Abhishek Kumar ;
    Markovic, Nikola
    2024-09-01 | PDF
    This study investigated the impact of roadside features on road departure (RD) crashes and provided recommendations for safety improvements on Utah's highways. The research utilized a computer vision model originally developed in the UDOT project, Automated Safety Assessment of Rural Roadways Using Computer Vision (Mashhadi et al., 2023), which was ...
  • Robust DecisionMaking Framework for Sustainable Operations and Planning of MBTA Rapid Transit Vehicles
    Robust Decision-Making Framework for Sustainable Operations and Planning of MBTA Rapid Transit Vehicles
    Han, Zhuo ;
    Christofa, Eleni
    2024-09-01 | PDF
    Urban Rail Transit (URT) systems play a critical role in modern cities but consume significant amounts of energy. Understanding and predicting energy consumption patterns in these systems is vital for sustainable urban planning, especially during disruptive events. This study presents a Long Short-Term Memory (LSTM) recurrent neural network, the mo ...
  • Visible Thermal Imaging and Deep Learning Based Approach for Automated Robust Detection of Potholes to Prioritize Highway Maintenance Brief
    Visible & Thermal Imaging and Deep Learning Based Approach for Automated Robust Detection of Potholes to Prioritize Highway Maintenance [Brief]
    Jia, Gaofeng ;
    Chen, Wei-Hsiang
    2024-09-01 | PDF
    Potholes are a significant pavement distress compromising safety and causing costly damage. They result from pavement deterioration due to aging, weather, and traffic overloads, with the Mountain Plains region particularly affected due to freeze/thaw cycles. Timely identification and repair of potholes are critical for effective highway maintenance ...
  • Visible Thermal Imaging and Deep Learning Based Approach for Automated Robust Detection of Potholes to Prioritize Highway Maintenance
    Visible & Thermal Imaging and Deep Learning Based Approach for Automated Robust Detection of Potholes to Prioritize Highway Maintenance
    Jia, Gaofeng ;
    Chen, Wei-Hsiang
    2024-09-01 | PDF
    Potholes are a significant pavement distress compromising safety and causing costly damage. They result from pavement deterioration due to aging, weather, and traffic overloads, with the Mountain Plains region particularly affected due to freeze/thaw cycles. Timely identification and repair of potholes are critical for effective highway maintenance ...
  • Studying the Use of LowCost Sensing Devices to Report Roadway Pavement Conditions
    Studying the Use of Low-Cost Sensing Devices to Report Roadway Pavement Conditions
    Abdallah, Moatassem ;
    Clevenger, Caroline M.
    2024-09-01 | PDF
    This report investigates the application of low-cost sensing technologies, including GPS, accelerometers, and smartphones, to monitor roadway pavement conditions in real time. By leveraging widely available sensors embedded in vehicles, this research demonstrates how machine learning models can detect and classify road anomalies, such as cracks and ...
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