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Reactive model for autonomous vehicles formation following a mobile reference

机译：遵循移动参考的自动驾驶车辆编队反应模型

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The emergence of collective motion in nature is ubiquitous and can be observed from colonies of bacteria to flocks of birds. The scientific community is interested in understanding how the local interactions drive the crowd toward global behaviors. This paper presents an agent-based reactive model for groups of vehicles that aims to make the formation to follow a moving reference, represented as a virtual agent. The model is called reactive because the agents do not keep previous information but only respond to the current system state. Moreover, they only communicate with their close neighbors, limited by their sensory radius, except with the virtual agent that can be seen by everyone at the whole time. The aim of the model is to group the agents around the virtual agent while it moves to desirable directions. We solve the inverse problem of parameter estimation in order to drive the model toward specific objectives. This task is performed with the Generalized Extremal Optimization (GEO) algorithm, and the results are tested with path planning scenarios.
机译：自然界中普遍存在集体运动，从细菌菌落到鸟群都可以观察到。科学界有兴趣了解本地互动如何推动人群走向全球行为。本文提出了一种用于车辆组的基于智能体的反应模型，旨在使编队遵循移动参考，表示为虚拟智能体。该模型称为反应式，因为代理不保留先前的信息，而仅响应当前的系统状态。而且，他们只与自己的近邻通信，受其感觉半径的限制，除了与虚拟代理人交流之外，虚拟代理人始终可以看到。该模型的目的是在虚拟代理移动到所需方向时对其进行分组。我们解决了参数估计的反问题，以便将模型推向特定目标。该任务使用广义极端优化（GEO）算法执行，并使用路径规划方案测试结果。

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