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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 161 - 180 of 613 results
  • Quantum Artificial IntelligenceSupported Trajectory Prediction for an Autonomous Truck Platoon
    Quantum Artificial Intelligence-Supported Trajectory Prediction for an Autonomous Truck Platoon
    Biswas, Pronab Kumar ;
    Salek, M Sabbir
    2024-12-01 | PDF
    Truck platooning can potentially increase the operational efficiency of freight movement on U.S. corridors, improving commercial productivity and economic vibrancy. Predicting the trajectory of each leading vehicle in an autonomous truck platoon using Artificial Intelligence (AI) can enhance platoon efficiency during unavailability of the real-time ...
  • The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation 2024 Updates
    The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation - 2024 Updates
    United States. Department of Transportation. Federal Highway Administration
    2024-12-01 | PDF
    This report, The U.S. Department of Transportation (USDOT) has prioritized developing technologies that promote traffic safety and improve mobility for all travelers. Supporting that USDOT priority, the Exploratory Advanced Research (EAR) Program of the Federal Highway Administration (FHWA) explores the development of Artificial Intelligence (AI) a ...
  • Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model
    Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model
    Ramakrishnan, Aravind ;
    Liu, Fangyu
    2024-12-01 | PDF
    For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market penetration level of truck platoons. The established framework uses a robust 3D pavement model, along wi ...
  • Investigation of UAS Platform and Personnel Development and Preparation through Lab and Site Testing
    Investigation of UAS Platform and Personnel Development and Preparation through Lab and Site Testing
    Almasi, Pouya ;
    Zhang, Qianyun
    2024-12-01 | PDF
    We propose a pilot program to initialize the UAS-based bridge inspection program in component level to enhance the bridge inspection practice in New Mexico State. In the proposed program, UAS-based bridge inspection strategies, data interpretation frameworks, and implementation software for individual NBI classified bridge components will be develo ...
  • Incorporating Large Language Models LLMs into Transportation Safety Analytics
    Incorporating Large Language Models (LLMs) into Transportation Safety Analytics
    Tang, Jinwei ;
    Luo, Yuebo
    2024-12-01 | PDF
    This project focuses on enhancing transportation safety through LLMs. Its goal is to improve crash data analysis by highlighting disparities in traffic safety, particularly for vulnerable road users. Leveraging the Connecticut Crash Data Repository, the project develops an LLM algorithm to automatically analyze crash patterns and generate visual re ...
  • Safe Reinforcement Learning for Intersection Management in RITI Communities Under Rare Extreme Events
    Safe Reinforcement Learning for Intersection Management in RITI Communities Under Rare Extreme Events
    Xiao, Yuanzhang
    2024-11-05 | PDF
    The rapid advancement of artificial intelligence (AI) is reshaping the transportation sector, with applications spanning autonomous vehicles, driver injury prevention, and traffic management. Efficient traffic management, particularly through adaptive intersection control, holds significant potential for reducing congestion. This study explores the ...
  • A Software Tool for Securing Deep Learning Against Adversarial Attacks for CAVs
    A Software Tool for Securing Deep Learning Against Adversarial Attacks for CAVs
    Pisu, Pierluigi ;
    Comert, Gurcan
    2024-11-01 | PDF
    This project focuses on the technological transfer of a robust perception algorithm previously developed to mitigate adversarial attacks, transforming it into a practical software tool with an intuitive interface. The initiative builds upon the prior project, Securing Deep Learning against Adversarial Attacks for Connected and Automated Vehicles, w ...
  • Optimal RWIS Sensor Density and Location Phase IV
    Optimal RWIS Sensor Density and Location - Phase IV
    Wu, Mingjian ;
    Kwon, Tae J
    2024-11-01 | PDF
    Road weather information systems (RWIS), in both stationary and mobile forms, have become increasingly popular in recent decades for their ability to collect and disseminate road weather and surface data. In addition to meteorological measurements, highway agencies rely heavily on RWIS imagery data to guide winter road maintenance (WRM) operations. ...
  • Lowcost RealTime Learningbased Localization for Autonomous Systems
    Low-cost Real-Time Learning-based Localization for Autonomous Systems
    Mangharam, Rahul
    2024-10-29 | PDF
    Robot localization is the problem of finding a robot's pose using a map and sensor measurements, like LiDAR scans or camera images. It is crucial for any moving autonomous vehicle to interact with the physical world correctly. However, finding injective mappings between measurements and poses is difficult because sensor measurements from multiple d ...
  • Anomaly Detection in Traffic Patterns Using the INDOT Camera System
    Anomaly Detection in Traffic Patterns Using the INDOT Camera System
    Christopher, Lauren ;
    Chien, Stanley
    2024-10-01 | PDF
    The Transportation and Autonomous Systems Institute (TASI) of Purdue University Indianapolis (PUI) and the INDOT Traffic Management Center worked together to develop a system that monitors traffic conditions using INDOT CCTV video feeds. Computer vision-based traffic anomaly detection has been studied for the past 20 years, and a thorough state-of- ...
  • Artificial Intelligence for Pedestrian and Bicyclist Safety Using AI To Detect NearMiss Collisions
    Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI To Detect Near-Miss Collisions
    Pourhomayoun, Mohammad
    ORCID ICON IMG
    2024-10-01 | PDF
    Near-Miss Collisions are events that, with a slight change in position or timing, could have resulted in a collision, which could have caused severe injury or property damage. Understanding near-miss collisions can help identify risks and potentially improve road safety. In this project, we developed an effective end-to-end system based on advanced ...
  • Winter Safety Improvement with Computer Vision and Transfer Learning
    Winter Safety Improvement with Computer Vision and Transfer Learning
    Ramezanpourkami, Moein ;
    Zhu, Xuan
    2024-10-01 | PDF
    The preservation of road safety in snowy regions during the winter season is of paramount significance due to the presence of perilous meteorological circumstances, such as snowstorms, which can result in escalated vehicular collisions and subsequent roadway closure. In the present investigation, our primary objective is to devise a novel methodolo ...
  • Winter Safety Improvement with Computer Vision and Transfer Learning Brief
    Winter Safety Improvement with Computer Vision and Transfer Learning [Brief]
    Ramezanpourkami, Moein ;
    Zhu, Xuan
    2024-10-01 | PDF
    The preservation of road safety in snowy regions during the winter season is of paramount significance due to the presence of perilous meteorological circumstances, such as snowstorms, which can result in escalated vehicular collisions and subsequent roadway closure. In the present investigation, our primary objective is to devise a novel methodolo ...
  • MobilePhoneBased Artificial Intelligence Package Development and Validation in Large Scale for Maintenance Asset Management
    Mobile-Phone-Based Artificial Intelligence Package Development and Validation in Large Scale for Maintenance Asset Management
    Kuang, Biao ;
    Chen, Jianli
    2024-10-01 | PDF
    Regular inspection of transportation assets is essential to maintain their optimal condition and functionality. Our previous UDOT-funded project (Contract No. 22-8099, Report No. UT-22.24) demonstrated significant promise of artificial intelligence in identifying various transportation assets through a mobile phone. However, the initial project was ...
  • Autonomous Rail Surface Defect Detection
    Autonomous Rail Surface Defect Detection
    Qian, Yu ;
    Guo, Huaqiang
    2024-09-30 | PDF
    The Autonomous Rail Surface Defect Detection project aims to enhance railway safety through the use of unmanned aerial vehicles (UAVs) for detecting rail surface defects. Using the RSD_UAV dataset, the project developed an optimized DeepLabv3-plus model with a ResNet-18 backbone and CBAM, achieving a mean Intersection over Union (mIOU) of 84.97% an ...
  • Grade Crossing Monitoring Using Deep Learning
    Grade Crossing Monitoring Using Deep Learning
    Ali, Gasser ;
    Tarawneh, Constantine
    2024-09-25 | PDF
    Railway crossings are critical elements of railway safety due to the heightened risk of collisions. Transportation agencies and researchers are continuously working to enhance safety at railway crossings with better operating procedures and equipment to avoid accidents. Many innovative methods have been proposed to detect hazards at crossings and r ...
  • AIEnabled Vibration Sensing System for Early Detection of Trains at Active HighwayRail Grade Crossings
    AI-Enabled Vibration Sensing System for Early Detection of Trains at Active Highway-Rail Grade Crossings
    Amjadian, Mohsen ;
    Tarawneh, Constantine
    2024-09-24 | PDF
    Highway-rail grade crossings (HRGCs) play an essential role in ensuring the secure traversal of road users across railway tracks. However, despite their significance, they present safety challenges, particularly when trains go undetected, heightening the risk of potential collisions between the road user and train. This study aims to explore the vi ...
  • Railroad Artificial Intelligence Intruder Learning System RAIILS
    Railroad Artificial Intelligence Intruder Learning System (RAIILS)
    Vega, Erick ;
    Lautala, Pasi
    2024-09-19 | PDF
    This report summarizes the research conducted as part of Phase 2 of the Railroad Artificial Intelligence Intruder Learning System (RAIILS), led by a team of researchers at Michigan Technological University (Michigan Tech) and sponsored by the Federal Railroad Administration (FRA) from March 2019 to September 2021. The project had two primary object ...
  • Generating SafetyCritical Driving Scenarios for the Design of the CAV ProvingGround Using Domain Knowledge Causality and Large Language Models
    Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground - Using Domain Knowledge, Causality, and Large Language Models
    Zhao, Ding
    ORCID ICON IMG
    2024-09-18 | PDF
    The goal of this project is to study the critical scenario generation, design a testing framework, and use the generated scenarios to help the design of autonomous vehicle proving ground infrastructure aiming at the connected and autonomous vehicles evaluation. These scenarios should reflect the critical factors and risky driving conditions in the ...
  • Pedestrian and Bicyclist Safety at HighwayRail Grade Crossings Year 1 Report
    Pedestrian and Bicyclist Safety at Highway-Rail Grade Crossings (Year 1 Report)
    Khattak, Aemal J. ;
    Farooq, Muhammad Umer
    2024-09-15 | PDF
    Published literature is sparse on non-motorist (pedestrians and bicyclists) crashes at Highway-rail Grade Crossings (HRGCs), despite their involvement in rail-related crashes. A critical aspect of crash prediction models for HRGs is crash exposure, which measures activities of interest at specific locations. While data on motor vehicle and train tr ...
161 - 180 / 613
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