Science Archaeology 1.3 million people share DNA with Maryland's earliest colonists Some are even related to the former colony's first governor. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The exterior of the reconstructed Catholic chapel at Historic St.Mary's City in St. Mary's City, Maryland. Breakthroughs, discoveries, and DIY tips sent six days a week. In 1634, English settlers established St. Mary's City as the first permanent outpost in the colony of Maryland.

Modeling and evaluation of automated vehicles (AVs) in mixed-autonomy traffic is essential prior to their safe and efficient deployment. This is especially important at urban junctions where complex multi-agent interactions occur. Current approaches for modeling vehicular maneuvers and interactions at urban junctions have limitations in formulating non-cooperative interactions and vehicle dynamics within a unified mathematical framework. Previous studies either assume predefined paths or rely on cooperation and central controllability, limiting their realism and applicability in mixed-autonomy traffic. This paper addresses these limitations by proposing a modeling framework for trajectory planning and decentralized vehicular control at urban junctions. The framework employs a bi-level structure where the upper level generates kinematically feasible reference trajectories using an efficient graph search algorithm with a custom heuristic function, while the lower level employs a predictive controller for trajectory tracking and optimization. Unlike existing approaches, our framework does not require central controllability or knowledge sharing among vehicles. The vehicle kinematics are explicitly incorporated at both levels, and acceleration and steering angle are used as control variables. This intuitive formulation facilitates analysis of traffic efficiency, environmental impacts, and motion comfort. The framework's decentralized structure accommodates operational and stochastic elements, such as vehicles' detection range, perception uncertainties, and reaction delay, making the model suitable for safety analysis. Numerical and simulation experiments across diverse scenarios demonstrate the framework's capability in modeling accurate and realistic vehicular maneuvers and interactions at various urban junctions, including unsignalized intersections and roundabouts.

The integration of autonomous vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency. However, understanding the interactions between AVs and human-driven vehicles (HVs) at intersections remains an open research question. This study aims to bridge this gap by examining behavioral differences and adaptations of AVs and HVs at unsignalized intersections by utilizing two comprehensive AV datasets from Waymo and Lyft. Using a systematic methodology, the research identifies and analyzes merging and crossing conflicts by calculating key safety and efficiency metrics, including time to collision (TTC), post-encroachment time (PET), maximum required deceleration (MRD), time advantage (TA), and speed and acceleration profiles. The findings reveal a paradox in mixed traffic flow: while AVs maintain larger safety margins, their conservative behavior can lead to unexpected situations for human drivers, potentially causing unsafe conditions. From a performance point of view, human drivers exhibit more consistent behavior when interacting with AVs versus other HVs, suggesting AVs may contribute to harmonizing traffic flow patterns. Moreover, notable differences were observed between Waymo and Lyft vehicles, which highlights the importance of considering manufacturer-specific AV behaviors in traffic modeling and management strategies for the safe integration of AVs. The processed dataset utilized in this study is openly published to foster the research on AV-HV interactions.

Current approaches to identifying driving heterogeneity face challenges in comprehending fundamental patterns from the perspective of underlying driving behavior mechanisms. The concept of Action phases was proposed in our previous work, capturing the diversity of driving characteristics with physical meanings. This study presents a novel framework to further interpret driving patterns by classifying Action phases in an unsupervised manner. In this framework, a Resampling and Downsampling Method (RDM) is first applied to standardize the length of Action phases. Then the clustering calibration procedure including ''Feature Selection'', ''Clustering Analysis'', ''Difference/Similarity Evaluation'', and ''Action phases Re-extraction'' is iteratively applied until all differences among clusters and similarities within clusters reach the pre-determined criteria. Application of the framework using real-world datasets revealed six driving patterns in the I80 dataset, labeled as ''Catch up'', ''Keep away'', and ''Maintain distance'', with both ''Stable'' and ''Unstable'' states. Notably, Unstable patterns are more numerous than Stable ones. ''Maintain distance'' is the most common among Stable patterns. These observations align with the dynamic nature of driving. Two patterns ''Stable keep away'' and ''Unstable catch up'' are missing in the US101 dataset, which is in line with our expectations as this dataset was previously shown to have less heterogeneity. This demonstrates the potential of driving patterns in describing driving heterogeneity. The proposed framework promises advantages in addressing label scarcity in supervised learning and enhancing tasks such as driving behavior modeling and driving trajectory prediction.

The sheer amount of data and its interconnectedness in 2020--along with the determination of cybercriminals to devise new ways to access it--will add up to trouble, cybersecurity experts warn. Clayton Calvert, a consultant at netlogx, an IT security and risk assessment firm, says 2020 will provide "an ever-evolving threat landscape." "There is no such thing as complete security, so enterprises are adopting cyber resiliency in order to bounce back quickly from continuous security breaches," Calvert says. He offers a number of ways that enterprises can be resilient. One way is to combine machine learning and automation with visibility to fight cyberattacks, such as through the use of security orchestration, automation and response (SOAR) products. "This type of machine learning helps reduce operational errors and helps enterprises self-manage, self-defend, and potentially self-heal against risks and breaches," Calvert says.

Los Angeles Mayor Eric Garcetti urges the public to ask their members of Congress to support continued federal funding of the earthquake early warning system. Los Angeles Mayor Eric Garcetti urges the public to ask their members of Congress to support continued federal funding of the earthquake early warning system. In this hyper-partisan era, there may be one issue that unites California Democrats and Republicans: Earthquakes. Elected officials from both parties have supported an earthquake early warning system for the West Coast that, after years of work, was scheduled to begin its first limited public operation next year. But President Trump's budget proposal calls for cuts that experts say would kill the warning network.