Cooperation is an enabler for autonomous vehicles. A promising application of cooperative driving is high-density platooning, where trucks drive with low inter-vehicle distances. It aims at increasing the road and fuel efficiency whilst guaranteeing safety. The safe and efficient coordination of the control requires the regular and reliable exchange of V2V messages. The performance of the vehicular application has been shown to be strongly affected by the variation of the performances of the communications system. To be able to adapt their functional settings to these variations, vehicles need the ability to predict it. We present a prediction model for the packet inter-reception time platoon messages in an IEEE 802.11p network. This performance indicator is the subject of extensive research as it captures the irregularity of input for the control loop. The prediction model uses conditional density estimation based on the exponential distribution. We fit this model using a multi-layer perceptron regressor based on features representing the surrounding communication environment. The presented results are based on data collected during a full scale platooning simulations using ns-3 and SUMO. We compare different environment abstraction models and show the potential of on-line learning.

中文翻译：

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基于周围流量分布的分组间接收时间条件密度估计

合作是自动驾驶汽车的推动力。协同驾驶的一个有希望的应用是高密度排，卡车在这种情况下，车辆之间的行驶距离很短。其目的是在保证安全的同时提高道路和燃油效率。安全有效的控制协调需要定期可靠地交换V2V消息。业已证明，车辆应用程序的性能受到通信系统性能变化的强烈影响。为了能够使其功能设置适应这些变化，车辆需要能够对其进行预测的能力。我们为IEEE 802.11p网络中的数据包接收时间排消息提供了一种预测模型。该性能指标是广泛研究的主题，因为它捕获了控制回路输入的不规则性。预测模型使用基于指数分布的条件密度估计。我们基于表示周围通信环境的特征，使用多层感知器回归器来拟合此模型。给出的结果基于使用ns-3和SUMO进行的大规模排模拟中收集的数据。我们比较了不同的环境抽象模型，并显示了在线学习的潜力。给出的结果是基于使用ns-3和SUMO进行的大规模排模拟中收集的数据。我们比较了不同的环境抽象模型，并显示了在线学习的潜力。给出的结果基于使用ns-3和SUMO进行的大规模排模拟中收集的数据。我们比较了不同的环境抽象模型，并显示了在线学习的潜力。