Several cooperative driving strategies proposed in literature, sometimes known as cooperative adaptive cruise control strategies, assume that both relative spacing and relative velocity with preceding vehicle are available from on-board sensors (laser or radar). Alternatively, these strategies assume communication of both velocity states and acceleration inputs from preceding vehicle. However, in practice, on-board sensors can only measure relative spacing with preceding vehicle (since getting relative velocity requires additional filtering algorithms); also, reducing the number of variables communicated from preceding vehicle is crucial to save bandwidth. In this work we show that, after framing the cooperative driving task as a distributed model reference adaptive control problem, the platooning task can be achieved in a minimal sensing and communication scenario, that is, by removing relative velocity measurements with preceding vehicle and by removing communication from preceding vehicle of velocity states. In the framework we propose, vehicle parametric uncertainty is taken into account by appropriately designed adaptive laws. The proposed framework is illustrated and shown to be flexible to several standard architectures used in cooperative driving (one-vehicle look-ahead topology, leader-to-all topology, multivehicle look-ahead topology).

中文翻译：

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一种自适应队列的最小感知和通信控制策略

文献中提出的几种协同驾驶策略，有时称为协同自适应巡航控制策略，假设与前车的相对间距和相对速度都可以从车载传感器（激光或雷达）获得。或者，这些策略假设来自前车的速度状态和加速度输入的通信。然而，在实践中，车载传感器只能测量与前车的相对间距（因为获得相对速度需要额外的滤波算法）；此外，减少从前车通信的变量数量对于节省带宽至关重要。在这项工作中，我们表明，在将协作驾驶任务构建为分布式模型参考自适应控制问题之后，队列任务可以在最小的传感和通信场景中实现，即通过删除与前车的相对速度测量值并通过从前车的速度状态中删除通信。在我们提出的框架中，通过适当设计的自适应定律考虑了车辆参数的不确定性。所提出的框架被说明并显示出对协同驾驶中使用的几种标准架构（单车前瞻拓扑、领先者对所有拓扑、多车前瞻拓扑）的灵活性。通过适当设计的自适应定律考虑车辆参数的不确定性。所提出的框架被说明并显示出对协同驾驶中使用的几种标准架构（单车前瞻拓扑、领先者对所有拓扑、多车前瞻拓扑）的灵活性。通过适当设计的自适应定律考虑车辆参数的不确定性。所提出的框架被说明并显示出对协同驾驶中使用的几种标准架构（单车前瞻拓扑、领先者对所有拓扑、多车前瞻拓扑）的灵活性。