WebJun 10, 2024 · A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. WebOct 16, 2024 · This is because state-of-the-art DRL-based networking solutions use standard neural networks (e.g., fully connected, convolutional), which are not suited to learn from information structured as graphs. In this paper, we integrate Graph Neural Networks (GNN) into DRL agents and we design a problem specific action space to …
GitHub - ahq1993/MPNet: Motion Planning Networks
WebJun 11, 2024 · It is demonstrated that GNNs can offer better results when compared to traditional analytic methods as well as learning-based approaches that employ fully-connected networks or convolutional neural networks. This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning … WebOct 24, 2024 · Graph Neural Networks (GNNs) are a popular choice of representation for motion planning problems, because of their capability to capture geometric information and are invariant to the permutations ... howl be quiet anime
[2006.06248] Graph Neural Networks for Motion Planning - arXiv.org
WebFeb 10, 2024 · Recently, Graph Neural Network (GNN) has gained increasing popularity in various domains, including social network, knowledge graph, recommender system, and even life science. The … WebChecking collision with obstacles is the major computational bottleneck in this process. We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perform path exploration and path smoothing. Given random geometric graphs (RGGs) generated from batch sampling ... how lb in a ton