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，该模型可同时为多车辆提供纵向和横向控制指令，目标是在非约束环境中确保每辆车到达期望目标状态的同时避免与其他车辆或障碍物发生碰撞。该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以做出明智决策，使每辆车既能顺利抵达目的地，又能有效避让其他智能体。训练此类网络所需的数据和对应标签通过基于优化的流程获取。实验结果表明，我们的模型具备强大的泛化能力，即使面对训练数据中未出现的更多车辆场景仍能应对。该方法还优于同类图神经网络架构。项目页面（含代码及补充信息）请访问 https://yininghase.github.io/multi-agent-control/