In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based Graphical Neural Network paradigm that takes into consideration the state of all the surrounding vehicles to make an informed decision. This allows each vehicle to smoothly reach its destination while also evading collision with the other agents. The data and corresponding labels for training such a network is obtained using an optimization based procedure. Experimental results demonstrates that our model is powerful enough to generalize even to situations with more vehicles than in the training data. Our method also outperforms comparable graphical neural network architectures. Project page which includes the code and supplementary information can be found at https://yininghase.github.io/multi-agent-control/

翻译：本文提出了一种基于学习的神经模型，能够同时为多辆车辆提供纵向和横向控制指令。该模型的目标是确保每辆车在无约束环境中不与其他车辆或障碍物发生碰撞，同时安全抵达目标状态。模型采用基于注意力的图神经网络范式，综合考虑周围所有车辆的状态来做出决策，从而使每辆车既能平稳到达目的地，又能规避与其他智能体的碰撞。训练该网络所需的数据和对应标签通过基于优化的方法获取。实验结果表明，该模型具有较强的泛化能力，甚至能够应对训练数据中未出现过的车辆数量更多的情况。我们的方法也优于同类图神经网络架构。项目页面（包含代码和补充信息）可访问 https://yininghase.github.io/multi-agent-control/ 查看。