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- W4384519152 abstract "Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multi-access Edge Computing (MEC) platform of 5 G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles' trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted." @default.
- W4384519152 created "2023-07-18" @default.
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- W4384519152 date "2023-01-01" @default.
- W4384519152 modified "2023-09-27" @default.
- W4384519152 title "Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance At Urban Intersections" @default.
- W4384519152 doi "https://doi.org/10.1109/tiv.2023.3296190" @default.
- W4384519152 hasPublicationYear "2023" @default.
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