Recent Submissions

  • Behavioral Indicators of Drowsy Driving: Active Search Mirror Checks 

    Meyer, Jason E.; Llaneras, Robert E. (Safe-D National UTC, 2022-07)
    Driver impairment due to drowsiness or fatigue has a significant impact on the safety of all road users. Assessing an impairment such as driver drowsiness through the use of vehicle-based technology continues to be an area ...
  • Crashworthiness Compatibility Investigation of Autonomous Vehicles with Current Passenger Vehicles 

    Dobrovolny, Chiara Silvestri; Stoeltje, Gretchen; Zalani, Aniruddha (Safe-D National UTC, 2021-11)
    Automated Vehicles have been one of the most sought-after concepts to make transportation more effective and safer. No-occupant vehicles with automated driving systems (ADS) make up one such class of vehicles. These are ...
  • Quantifying the Benefits and Harms of Connected and Automated Vehicle Technologies to Public Health and Equity 

    Dadashova, Bahar; Sohrabi, Soheil; Khreis, Haneen; Sener, Ipek; Zmud, Johanna (Safe-D National UTC, 2021-07)
    Automated Vehicles (AVs) have the potential to improve traffic safety by preventing crashes. The safety implications of AVs can vary across communities with different socioeconomic and demographic characteristics. In this ...
  • An Evaluation of Road User Interactions with E-Scooters 

    Hong, Yubin; Klauer, Charlie; Vilela, Jean Paul Talledo; Miles, Melissa (Safe-D National UTC, 2022-06)
    Electric scooters (e-scooters) are gaining in popularity due to their availability, accessibility, and low cost. However, there has been little research on how e-scooters behave on the road and interact with other road ...
  • Identifying Deviations from Normal Driving Behavior 

    Alambeigi, Hananeh; McDonald, Anthony D.; Shipp, Eva; Manser, Michael (Safe-D National UTC, 2022-01)
    One of the critical circumstances in automated vehicle driving is transition of control between partially automated vehicles and drivers. One approach to enhancing the design of transition of control is to predict driver ...
  • Evaluation of Transportation Safety Against Flooding in Disadvantaged Communities 

    Tavakol-Davani, Hassan; O'Hara-Rhi, Vincent T.; Machiani, Sahar Ghanipoor (Safe-D National UTC, 2022-05)
    Flooding in urban areas, especially in low-income or disadvantaged communities, poses a serious problem to drivers. While techniques exist to map and predict flooding events, a knowledge gap exists in accurate mapping and ...
  • A Sensor Fusion and Localization System for Improving Vehicle Safety in Challenging Weather Conditions 

    Singh, Abhay; Vegamoor, Vamsi Krishna; Rathinam, Sivakumar (Safe-D National UTC, 2021-12)
    SAE Level 5 autonomy requires the autonomous vehicle to be able to accurately sense the environment and detect obstacles in all weather and visibility conditions. This sensing problem becomes significantly challenging in ...
  • Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data 

    Guo, Feng; Qian, Chen; Shi, Liang (Safe-D National UTC, 2022-04)
    The emerging connected vehicle and Automated Driving System (ADS), the widely available advanced in-vehicle telematics data collection/transmitting systems, as well as smartphone apps produce gigantic amount of high-frequency, ...
  • Radar and LiDAR Fusion for Scaled Vehicle Sensing 

    Beale, Gregory T.; Berkemeier, Matthew D.; Doerzaph, Zachary R.; Perez, Miguel A. (Safe-D National UTC, 2022-05)
    Scaled test beds are popular tools to develop and physically test algorithms for advanced driving systems, but they often lack automotive-grade radars in their sensor suites. To overcome resolution issues when using a radar ...
  • Data Fusion for Nonmotorized Safety Analysis 

    Sener, Ipek N.; Munira, Silvy; Zhang, Yunlong (Safe-D National UTC, 2021-08)
    This project explored an emerging research territory, the fusion of nonmotorized traffic data for estimating reliable and robust exposure measures. Fusion mechanisms were developed to combine five bike demand data sources ...
  • Impacts of Connected Vehicle Technology on Automated Vehicle Safety 

    Herbers, Eileen; Stowe, Loren (Safe-D National UTC, 2022-05)
    Connected vehicle technologies have a promising role in advancing vehicle safety, but just how much of an impact can connected vehicles have on driver safety? This study uses crash and near-crash events from the Second ...
  • Creating a Smart Connected Corridor to Support Research into Connected and Automated Vehicles 

    Brydia, Robert; Ruback, Leonard; Middleton, Dan (Safe-D National UTC, 2022-06)
    As connected and automated vehicle (CAV) technologies rapidly advance from concept to in-vehicle testing, real-world testbeds equipped with the appropriate technologies to support testing of these vehicles is a requirement.Testing ...
  • 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, Alexander Charles; 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 E.; Mollenhauer, Michael A.; 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 ...

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