Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections
<|reference_start|>Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections: 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 5G 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.<|reference_end|>
@article{selvaraj2024edge-assisted,
    title={Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at
  Urban Intersections},
    author={Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou, Panayiotis
  Kolios, Carla Fabiana Chiasserini, and Georgios Ellinas},
    journal={arXiv preprint arXiv:2404.14523},
    year={2024},
    doi={10.1109/TIV.2023.3296190},
    archivePrefix={arXiv},
    eprint={2404.14523},
    primaryClass={cs.LG}
}