Browsing by Author "Li, Xiao"

Now showing 1 - 5 of 5
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    Autonomous Vehicles for Small Towns: Exploring Perception, Accessibility, and Safety
    Li, Wei; Ye, Xinyue; Li, Xiao; Dadashova, Bahar; Ory, Marcia G.; Lee, Chanam; Rathinam, Sivakumar; Usman, Muhammad; Chen, Andong; Bian, Jiahe; Li, Shuojia; Du, Jiaxin (Safe-D University Transportation Center, 2023-09)
    As of 2021, there were 18,696 small towns in the US with a population of less than 50,000. These communities typically have a low population density, few public transport services, and limited accessibility to daily services. This can pose significant challenges for residents trying to fulfill essential travel needs and access healthcare. Autonomous vehicles (AVs) have the potential to provide a convenient and safe way to get around without requiring human drivers, making them a promising transportation solution for these small towns. AV technology can become a first-line mobility option for people who are unable to drive, such as older adults and those with disabilities, while also reducing the cost of transportation for both individuals with special needs and municipalities. The report includes our research findings on 1) how residents in small towns perceive AV, including both positive and negative aspects; 2) the impacts of ENDEAVRide—a novel “Transport + Telemedicine 2-in-1” microtransit service delivered on a self-driving van in central Texas—on older adults’ travel and quality of life; and 3) the potential safety implications of AVs in small towns. This report will help municipal leaders, transportation professionals, and researchers gain a better understanding of how AV deployment can serve small towns.
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    Connected Vehicle Data Safety Applications
    Martin, Michael; Wu, Lingtao; Ramezani, Mahin; Li, Xiao; Turner, Shawn; Stutes, Sophia; Hasan, Faiza; Potter, Michael (2023-09)
    The large-scale assessment of how driving behavior affects traffic safety and ongoing surveillance is hindered by data collection difficulties, small sample sizes, and high costs. Connected vehicles (CV) now offer massive volumes of observed driving behavior data from newer vehicles with myriad electronics and sensors that monitor the state of the vehicle, environmental conditions, and the driver’s actions. This project evaluated the viability of CV data in roadway safety applications with the objective of improving existing predictive crash methods, measuring traffic speed and its relationship to crashes, and determining whether CV data could be used to evaluate pavement marking products. The research team developed safety performance functions (SPFs) for rural two-lane segments and urban intersections in Texas. The results showed that the SPFs improved with the addition of hard braking and hard acceleration counts in a majority of areas. Further, a variety of CV speed measures were generated from the CV data and were shown to have conflicting correlations with crash risk and counts. Lastly, the research team developed the data processing methods for evaluating pavement marking products but was unable to perform an evaluation due to the lack of detailed construction project records.
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    Exploring Crowdsourced Monitoring Data for Safety
    Turner, Shawn M.; Martin, Michael W.; 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 StreetLight Data and found promising correlations (R2 of 62% and 69% for monthly weekday and weekend daily averages) when the StreetLight data were compared to bicyclist counts from 32 locations in eight Texas cities, and even better correlation (R2 of 94%) when compared with countywide Strava data expanded to represent total bicycling activity. The second effort evaluated the pedestrian counting accuracy of the Miovision system and found 15% error for daytime and 24% error for nighttime conditions. The third effort used INRIX trip trace data to determine origin-destination patterns and developed 40 decision rules to define the origin-destination patterns. The fourth effort analyzed crowdsourced Waze data (i.e., traffic incidents) and found it to be a reliable alternative to observed and predicted crashes, with the ability to identify high-risk locations: 77% of high-risk locations identified from police-reported crashes were also identified as high-risk in Waze data. The researchers propose a method to treat the redundant Waze reports and to match the unique Waze incidents with police crash reports.
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    Structural Design of a 6-DoF Hip Exoskeleton using Linear Series Elastic Actuators
    Li, Xiao (Virginia Tech, 2017-08-28)
    A novel hip exoskeleton with six degrees of freedom (DoF) was developed, and multiple prototypes of this product were created in this thesis. The device was an upper level of the 12-DoF lower-body exoskeleton project, which was known as the Orthotic Lower-body Locomotion Exoskeleton (OLL-E). The hip exoskeleton had three motions per leg, which were roll, yaw, and pitch. Currently, the sufferers of hemiplegia and paraplegia can be addressed by using a wheelchair or operating an exoskeleton with aids for balancing. The motivation of the exoskeleton project was to allow paraplegic patients to walk without using aids such as a walker or crutches. In mechanical design, the hip exoskeleton was developed to mimic the behavior of a healthy person closely. The hip exoskeleton will be fully powered by a custom linear actuator for each joint. To date, there are no exoskeleton products that are designed to have all of the hip joints powered. Thus, packaging of actuators was also involved in the mechanical design of the hip exoskeleton. As a result, the output torque and speed for the roll joint and yaw joint were calculated. Each hip joint was structurally designed with properly selected bearings, encoder, and hard stops. Their range of motions met desired requirements. In addition, a backpack assembly was designed for mounting the hardware, such as cooling pumps, radiators, and batteries. In the verification part, finite element analysis (FEA) was conducted to show the robustness of the structural design. For fit testing, three wearable prototypes were produced to verify design choices. As a result, the weight of the current hip exoskeleton was measured as 32.1 kg.
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    Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework
    Abedi, Vida; Khan, Ayesha; Chaudhary, Durgesh; Misra, Debdipto; Avula, Venkatesh; Mathrawala, Dhruv; Kraus, Chadd; Marshall, Kyle A.; Chaudhary, Nayan; Li, Xiao; Schirmer, Clemens M.; Scalzo, Fabien; Li, Jiang; Zand, Ramin (2020-08)
    Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.