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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 221 - 240 of 613 results
  • Hierarchical Decision Making and Control in RLbased Autonomous Driving for Improved Safety in Complex Traffic Scenarios Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
    Hierarchical Decision Making and Control in RL-based Autonomous Driving for Improved Safety in Complex Traffic Scenarios - Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
    Redmill, Keith ;
    Zhang, Zhihao
    2024-07-31 | PDF
    Developing an automated driving system capable of navigating complex traffic environments remains a formidable challenge. Unlike rule-based or supervised learning-based methods, Deep Reinforcement Learning (DRL) based controllers eliminate the need for domain-specific knowledge and datasets, thus providing adaptability to various scenarios. Nonethe ...
  • Enhancing Traffic Safety and Connectivity A DataDriven MultiStepAhead Vehicle Headway Prediction Leveraging HighResolution Vehicular Trajectories
    Enhancing Traffic Safety and Connectivity: A Data-Driven Multi-Step-Ahead Vehicle Headway Prediction Leveraging High-Resolution Vehicular Trajectories
    Noruzoliaee, Mohamadhossein ;
    Nazari, Fatemeh
    2024-07-30 | PDF
    Vehicle headway, defined as the time elapsed between two successive vehicles passing a roadway point, is a key mesoscopic-scale measure in traffic flow theory with safety-critical transportation applications, such as preemptive collision avoidance warning systems as well as connected and autonomous vehicle (CAV) platoon control. Hence, it is crucia ...
  • Artificial Intelligence AI and Markov Process Based Data Mining on Predicting Bridge Operating Conditions
    Artificial Intelligence (AI) and Markov Process Based Data Mining on Predicting Bridge Operating Conditions
    Fang, F Clara ;
    JimenezGil, Daniel
    2024-07-22 | PDF
    To facilitate bridge management decision-making and implement preventive maintenance strategies, we endeavor to develop a machine learning model to predict the future condition rating of bridges using historical inspection data, with a funding contract with Connecticut State's Department of Transportation. We collect the data from the USA's Nationa ...
  • Systemic Noteworthy Practice Ohios Machine Learning Profile Analysis for Roadway Departure and Pedestrian Crashes
    Systemic Noteworthy Practice: Ohio's Machine Learning Profile Analysis for Roadway Departure and Pedestrian Crashes
    United States. Department of Transportation. Federal Highway Administration
    2024-07-12 | PDF
    A systemic approach to safety considers both risk and crash history when making decisions on where to implement low-cost safety improvements. This approach relies heavily on data to identify locations for safety improvements that may not have been considered using other analysis methods. Analyzing the entire system using a defined set of criteria c ...
  • Employing Artificial Intelligence AI To Enhance Infrastructure Inspections Fact Sheet
    Employing Artificial Intelligence (AI) To Enhance Infrastructure Inspections [Fact Sheet]
    United States. Department of Transportation. Federal Highway Administration
    2024-07-01 | PDF
    This fact sheet highlights the employing of artificial intelligence (AI) can be used to enhance infrastructure inspections. Assessing reinforced concrete structures for cracking, crack widths, patterns, and orientations usually involves some form of visual inspection. The Advanced Vision-Based Assessment of Infrastructure Systems project is develop ...
  • Development of the Roadway Pothole Management Program
    Development of the Roadway Pothole Management Program
    Cheng, DingXin
    2024-07-01 | PDF
    Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving dir ...
  • Assessing Pedestrian Safety on Roads Through Machine Learning Approaches for State Highways in Washington State
    Assessing Pedestrian Safety on Roads Through Machine Learning Approaches for State Highways in Washington State
    Wang, Yinhai ;
    Sun, Wei
    2024-07-01 | PDF
    The report presents a unique contrast for several Machine Learning approaches aiming at understanding pedestrian fatal collisions. Four classification techniques are applied to assess how roadway features mainly correlate to pedestrian fatal crashes: Logistic Regression, Nearest Neighbor Classification, Decision Tree, and Random Forest Classifier. ...
  • Data Acquisition and Processing Using Artificial Intelligence and Machine Learning
    Data Acquisition and Processing Using Artificial Intelligence and Machine Learning
    Loos, Ryan ;
    Egge, Mark
    2024-07-01 | PDF
    Artificial intelligence (AI) and machine learning (ML) offer state transportation agencies promising tools to help them advance their missions by informing decision making; improving information accuracy, completeness, and timeliness; and automating tedious tasks to free up valuable DOT resources. The objective of this research was to provide MoDOT ...
  • Development of the Roadway Pothole Management Program Research Brief
    Development of the Roadway Pothole Management Program [Research Brief]
    Cheng, DingXin
    2024-07-01 | PDF
    Road infrastructure in California has been a source of concern as highlighted by the 2021 Report Card for America's Infrastructure. The report assigned a grade of "D" to the roads. The assessment was based on the condition of California roads, which was among the worst in the nation, ranking 49th according to the latest US News & World Report Ranki ...
  • Data Acquisition and Processing Using Artificial Intelligence and Machine Learning Research Summary
    Data Acquisition and Processing Using Artificial Intelligence and Machine Learning [Research Summary]
    Loos, Ryan
    ORCID ICON IMG
    2024-07-01 | PDF
    This research provides MoDOT with an opportunity to "kick the tires" with AI and ML and better understand how these powerful technologies can help the agency meet its present and future challenges. This report-and the research activities preceding it-endeavors to equip MoDOT with the necessary knowledge and evaluative framework to effectively asses ...
  • Automated Interpretation of Culvert Inspection Videos Using AI and Computer Vision
    Automated Interpretation of Culvert Inspection Videos Using AI and Computer Vision
    Mohammadi, Pouria ;
    Rashidi, Abbas
    2024-07-01 | PDF
    Culvert assets play a critical role in ensuring the safe operation of highways. UDOT maintains over 120,000 drainage culverts and storm drain pipes along state highways. To maintain these assets optimally and prevent failures, UDOT must collect comprehensive information about all culverts across the state and inventory them in the ATOM system. Accu ...
  • Development of a Salt Spreader Controller Program Using MachineSensed Roadway Weather Parameters
    Development of a Salt Spreader Controller Program Using Machine-Sensed Roadway Weather Parameters
    Ai, Chengbo ;
    Tessier, Russell
    2024-07-01 | PDF
    Massachusetts owns over 300 and contracts 1,200 additional material spreaders that deliver salt, salt, sand mixtures, or liquid deicers to more than 15,000 lane miles of roadways during winter seasons. It is critical to efficiently and effectively deliver the materials so that the impacts of winter storms on road operations are minimized while the ...
  • Integrating NonMotorist Facility Data into Comprehensive Road Safety Assessment
    Integrating Non-Motorist Facility Data into Comprehensive Road Safety Assessment
    Qin, Xiao ;
    Schneider, Robert J.
    2024-07-01 | PDF
    As active transportation gains popularity for both work and leisure trips, the need for reliable and accurate data on non-motorist activities and infrastructure becomes increasingly critical. Traditionally, detailed data on non-motorist activities and facilities has been scarce and challenging to obtain. However, advancements in technology now prov ...
  • Utilizing Daily Traffic as a Sensor Network for Infrastructure Health Monitoring
    Utilizing Daily Traffic as a Sensor Network for Infrastructure Health Monitoring
    Pakzad, Shamim N ;
    Takac, Martin
    2024-07-01 | PDF
    Mobile sensing is a novel paradigm that offers numerous advantages over conventional stationary sensor networks for real time bridge monitoring. Mobile sensors have low setup costs, collect spatio-temporal information efficiently, and require no dedicated sensors to any particular structure. Most importantly, they can capture comprehensive spatial ...
  • Using Artificial Intelligence to Evaluate Pavement Condition and Safety Fact Sheet
    Using Artificial Intelligence to Evaluate Pavement Condition and Safety [Fact Sheet]
    United States. Department of Transportation. Federal Highway Administration
    2024-07-01 | PDF
    This factsheet highlights the project sponsored by Exploratory Advanced Research (EAR), that seeks to enhance automation of pavement information collection for use in pavement condition safety and evaluation at highway speed. Through this project, titled Artificial Intelligence Approaches to Multi-Object Evaluations of Pavement for Condition and Sa ...
  • Remote Sensing of Multimodal Transportation Assets Using Drones Brief
    Remote Sensing of Multimodal Transportation Assets Using Drones [Brief]
    Bridgelall, Raj ;
    Tolliver, Denver
    2024-07-01 | PDF
    Drone technologies offer a paradigm shift in railroad inspection and monitoring, enhancing operational efficacy, reducing carbon footprint, and mitigating safety hazards. Similarly, drones, especially when integrated with real-time kinematic global positioning systems and artificial intelligence, offer a compelling solution for efficient roadway co ...
  • Using Artificial Intelligence to Improve Safety for Vulnerable Road Users
    Using Artificial Intelligence to Improve Safety for Vulnerable Road Users
    United States. Federal Highway Administration. Exploratory Advanced Research Program
    2024-07-01 | PDF
    This comprehensive plan embraces the Safe System Approach, which includes five elements that need to be addressed to achieve zero traffic deaths: safe road users, safe vehicles, safe speeds, safe roads, and post-crash care. The risk of fatality and injury for vulnerable road users (VRUs) (i.e., any road user not in a motor vehicle with a protected ...
  • Intelligent Asset Management for Improved Mobility Technology Transfer for South Carolina
    Intelligent Asset Management for Improved Mobility: Technology Transfer for South Carolina
    Ziehl, Paul ;
    Ai, Li
    2024-07-01 | PDF
    This report details the implementation of digital twin technology for bridge load rating in South Carolina, focusing on developing a graphical user interface (GUI). Traditional bridge load rating methods are highlighted for their high costs, time consumption, and traffic disruptions. To address these issues, the research team introduced a digital t ...
  • NonContact Intelligent Inspection of Infrastructure
    Non-Contact Intelligent Inspection of Infrastructure
    Tang, Jiong ;
    Zhang, Yang
    2024-07-01 | PDF
    In this project, we develop non-contact sensing mechanism for health monitoring as well as the associated machine-learning based technique for decision making. Currently available sensory systems for structural health monitoring are almost all based on transducers that are directly attached to or embedded in structures monitored. As a result, they ...
  • Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor Data
    Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor Data
    Zhang, Guohui ;
    Zhou, Shanglian
    2024-06-30 | PDF
    Recent technological advancements in computer vision algorithms and data acquisition devices have greatly facilitated research activities towards enhancing traffic sensing for traffic safety performance improvements. Significant research efforts have been devoted to developing and deploying more effective technologies to detect, sense, and monitor ...
221 - 240 / 613
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