Transportation
For optimizing RSU deployment, comprehensive data is essential. Geographical data includes detailed road network topology, traffic flow patterns, and geographic coordinates. Communication range data covers effective ranges of RSUs and connected vehicles, considering signal strength and environmental conditions. Vehicle data involves classifications based on communication capabilities and percentages of connected vehicles. Network performance data includes latency requirements, packet loss rates, and bandwidth needs.
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Despite the considerable efforts to enhance road infrastructure and enforce stricter driving regulations to ensure road safety, the number of accidents worldwide remains alarmingly high, driven by factors such as distracted driving, speeding, and impaired driving. For instance, in the United States, fatal accidents increased by 16% from 2018 to 2022, with the number of fatalities rising from 36,835 in 2018 to 42,795 in 2022. This highlights the pressing need for innovative solutions to mitigate traffic incidents and enhance road safety.
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This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions. We define an intention-aware control problem incorporating epistemic uncertainties of the opponent vehicles and model their intentions as discrete-valued random variables. Then, we focus on a control objective specified as belief-space temporal logic specifications. From this stochastic control problem, we derive a sound deterministic control problem using stochastic expansion and solve it using shrinking-horizon model predictive control.
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This dataset reports information on Metanet model parameters and traffic conditions on the intercity bus line in Liguria (Italy) that connects Savona with FInalborgo. The dataset also contains information resulting from the model execution, reporting flows, speeds, densities, and boundary conditions of two traffic scenarios corresponding to two different periods of the day (7 am-9 am) (2 pm-4 pm)
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The advancement of machine and deep learning methods in traffic sign detection is critical for improving road safety and developing intelligent transportation systems. However, the scarcity of a comprehensive and publicly available dataset on Indian traffic has been a significant challenge for researchers in this field. To reduce this gap, we introduced the Indian Road Traffic Sign Detection dataset (IRTSD-Datasetv1), which captures real-world images across diverse conditions.
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Abstract-In this paper a FSS-based absorber:with highabsorption effciency is proposed in the terahertz regime foruse in communications applications. It has the potential toenhance the performance of communication systems, minimizesignal interference, and guarantee the stability and effciencyof data transmission, The structure of this terahertz absorbercomprises annular patches and shaped patches.
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Modern automotive embedded systems include a large number of electronic control units (ECU) responsible for managing sophisticated systems such as engine control, ABS brake systems, traction control, and power steering systems. To ensure the reliability and effectiveness of these functions, it is essential to apply rigorous test approaches and standards. The integration of diagnostic functions in automotive embedded systems demands consistent tests and a detailed analysis of data.
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This dataset provides valuable insights into Received Signal Reference Power (RSRP) measurements collected by User Equipment (UE) devices strategically positioned within a moving train, featuring the hexagonal frequency selective pattern on its windows. Additionally, it includes RSRP values obtained from an external reference source using the rooftop train antenna.
All the data in this dataset corresponds to the research conducted in our work titled "Enhancing Mobile Communication on Railways: Impact of Train Window Size and Coating".
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