Browsing by Author "Li, Tianyi"

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    Assessing the Energy Impacts of Cyberattacks on Low-Level Automated Vehicles
    (Center for Transportation Studies, University of Minnesota, 2023-08) Stern, Raphael; Li, Tianyi; Rosenblad, Benjamin; Shang, Mingfeng
    In this study, we investigate the potential impact of stealthy cyberattacks on automated or partially automated vehicles, and consider how they will influence traffic flow and fuel consumption. Specifically, we define stealthy cyberattacks on automated vehicles where driving behavior deviates only slightly from normal driving behavior. We use simulation analysis to consider different cyberattacks, and investigate their impact on traffic flow and aggregate fuel consumption of all vehicles in the traffic flow. We find that such attacks, while difficult to detect, may substantially degrade traffic flow, and, to a lesser extent, vehicle emissions across the traffic flow.
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    Guidelines for safer pedestrian crossings: Understanding the factors that positively influence vehicle yielding to pedestrians at unsignalized intersections
    (Minnesota Department of Transportation, 2023-06) Stern, Raphael; Li, Tianyi
    Many factors influence an individual driver's decision to yield or not yield to individual pedestrians attempting to cross the road at an unsignalized crossing. This study collects observational data from more than 3,300 crossing events at 18 intersections in Minnesota to further our understanding of what factors positively influence driver yielding. Using the collected data, a statistical analysis was conducted to identify features that most strongly correlate with driver yielding. Event specific features such as speed were found to greatly influence yielding, with vehicles traveling at a speed of greater than 25 mph significantly less likely to yield to pedestrians than vehicles traveling at speeds lower than 25 mph. Site-specific features such as the presence of signs indicating a crossing were also strongly correlated with driver yielding. The results provide indication of which features of unsignalized crossings correlate with higher driver yielding rates. These findings can be used to guide policy and design at sites where a high driver yielding rate is desirable.
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