Recent Submissions

  • Predicting Vehicle Trajectories at Intersections using Advanced Machine Learning Techniques 

    Jazayeri, Mohammad Sadegh; Jahangiri, Arash; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems that aim to identify critical events such as near-crash situations and traffic violations. In a connected environment, ...
  • Data Mining Twitter to Improve Automated Vehicle Safety 

    McDonald, Anthony D.; Huang, Bert; Wei, Ran; Alambeigi, Hananeh; Arachie, Chidubem; Smith, Alec; Jefferson, Jacelyn (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly ...
  • Development of a Connected Smart Vest for Improved Roadside Work Zone Safety 

    Roofigari-Esfahan, Nazila; White, Elizabeth; Mollenhauer, Michael; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-04)
    Roadside work zones (WZs) present imminent safety threats for roadway workers as well as passing motorists. In 2016, 764 fatalities occurred in WZs in the United States due to motor vehicle traffic crashes. A number of ...
  • Reference Machine Vision for ADAS Functions 

    Nayak, Abhishek; Rathinam, Sivakumar; Pike, Adam (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning and Lane Keep Assist systems are used effectively. However, these systems have not been ...
  • Design and Development of an Automated Truck Mounted Attenuator 

    White, Elizabeth E.; Mollenhauer, Michael A.; Talledo Vilela, Jean Paul (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    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. While the TMA is designed ...
  • Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data 

    Liang, Dan; Antin, Jonathan F.; Lau, Nathan K.; Stulce, Kelly E. (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    The study examined whether advanced driver assistance systems (ADAS) can benefit the mobility and driving performance of senior drivers. Two groups of driving data, collected separately from two naturalistic driving projects, ...
  • Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements 

    Tsapakis, Ioannis; Das, Subasish; Khodadadi, Ali; Lord, Dominique; Morris, Jessica; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-03)
    States are required to have access to annual average daily traffic (AADT) for all public paved roads, including non-federal aid system (NFAS) roadways. The expectation is to use AADT estimates in data-driven safety analysis. ...
  • Safety Impact Evaluation of a Narrow-Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway 

    Machiani, Sahar Ghanipoor; Jahangiri, Arash; Melendez, Benjamin; Katthe, Anagha; Hasani, Mahdie; Ahmadi, Alidad; Musial, Walter B. (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    This study fills the gap in the limited research on the effect of emerging Automated Vehicle (AV) technology on infrastructure standards. The main objective of this research is to evaluate implications of an innovative ...
  • Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles 

    Talebpour, Alireza; Lord, Dominique; Manser, Michael; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but ...
  • Modeling Driver Behavior During Automated Vehicle Platooning Failures 

    McDonald, Anthony D.; Sarkar, Abhijit; Hickman, Jeffrey; Alambeigi, Hananeh; Vogelpohl, Tobias; Markkula, Gustav (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-01)
    Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, ...
  • Analysis of an Incentive-Based Smartphone Application for Young Drivers 

    Henk, Russell H.; Munira, Sirajum; Tisdale, Stacey (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-01)
    Traffic crashes remain the leading cause of unintentional youth deaths and injuries across the United States. Development of new and innovative interventions continues, with the aim of addressing this public health issue ...
  • Autonomous Emergency Navigation to a Safe Roadside Location 

    Furukawa, Tomonari; Zuo, Lei; Parker, Robert G.; Yang, Lisheng (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    In this project, we developed essential modules for achieving the proposed autonomous emergency navigation function for an automated vehicle. We investigated and designed sensing solutions for safe roadside location ...
  • Real-World Use of Automated Driving Systems and their Safety Consequences: A Naturalistic Driving Data Analysis 

    Kim, Hyungil; Song, Miao; Doerzaph, Zachary R. (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Automated driving systems (ADS) have the potential to fundamentally change transportation, and a growing number of these systems have entered the market and are currently in use on public roadways. However, drivers may not ...
  • Response of Autonomous Vehicles to Emergency Response Vehicles (RAVEV) 

    Nayak, Abhishek; Rathinam, Sivakumar; Gopalswamy, Swaminathan (SAFE-D: Safety Through Disruption National UTC, 2020-06)
    The objective of this project was to explore how an autonomous vehicle identifies and safely responds to emergency vehicles using visual and other onboard sensors. Emergency vehicles can include police, fire, hospital and ...
  • Big Data Visualization and Spatiotemporal Modeling of Risky Driving 

    Jahangiri, Arash; Marks, Charles; Machiani, Sahar Ghanipoor; Nara, Atsushi; Hasani, Mahdie; Cordova, Eduardo; Tsou, Ming-Hsiang; Starner, Joshua (SAFE-D: Safety Through Disruption National UTC, 2020-07)
    Statistical evidence shows the role of risky driving as a contributing factor in roadway collisions, highlighting the importance of identifying such driving behavior. With the advent of new technologies, vehicle kinematic ...
  • Assessing Alternate Approaches for Conveying Automated Vehicle ‘Intentions’ 

    Basantis, Alexis; Miller, Marty; Doerzaph, Zachary; Neurauter, Luke (SAFE-D: Safety Through Disruption National UTC, 2020-05)
    One of the biggest highly automated vehicle (HAV) market barriers may be a lack of user trust in the automated driving system itself. Research has shown that this lack of faith in the system primarily stems from a lack of ...
  • Development of Analytic Method to Determine Weaving Patterns for Safety Analysis near Freeway Interchanges with Access Management Treatments 

    Dastgiri, Maryam Shirinzadeh; Dixon, Karen K. (SAFE-D: Safety Through Disruption National UTC, 2020-07)
    Urban arterials near freeway interchanges are vital elements of urban road infrastructures. They connect freeway network with high mobility and low access to urban network with lower mobility and higher access. This study ...
  • Identification of Railroad Requirements for the Future Automated and Connected Vehicle (AV/CV) Environment 

    Morgan, Curtis A.; Warner, Jeffery E.; Lee, Dahye; Florence, David (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-06)
    The Federal Rail Administration (FRA) Highway-Rail Grade Crossing Inventory database from 2019 states that there are approximately 127,000 public, at-grade highway-rail grade crossings in the U.S. Despite this large number ...
  • Exploring Crowdsourced Monitoring Data for Safety 

    Turner, Shawn; Martin, Michael; Griffin, Greg P.; Le, Minh; Das, Subasish; Wang, Ruihong; Dadashova, Bahar; Li, Xiao (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-03)
    This project included four distinct but related exploratory studies of data sources that could improve roadway safety analysis. The first effort evaluated passively gathered crowdsourced bicyclist activity data from ...
  • Formalizing Human Machine Communication in the Context of Autonomous Vehicles 

    Gopalswamy, Swaminathan; Saripalli, Srikanth; Shell, Dylan; Hickman, Jeff; Hsu, Ya-Chuan (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-05)
    There are many situations where tacit communication between drivers and pedestrians governs and enhances safety. The goal of this study was to formalize this communication and apply it to the driving strategy of an autonomous ...

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