Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module
Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module
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DOI：
10.1109/wcnc45663.2020.9120722
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Yuru Li;Dongliang Duan;Chen Chen-Chen;Xiang Cheng;Liuqing Yang
中科院分区：
文献类型：
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作者：
Yuru Li;Dongliang Duan;Chen Chen-Chen;Xiang Cheng;Liuqing Yang
对于智能交通系统中的无人车自主驾驶，车载网络支持的多车协同感知可以大大提高感知决策的准确性和可靠性。目前，单个车辆的感知决策大多由神经网络提供。因此，为了融合来自多个车辆的感知决策，需要研究神经网络输出的可信度。在众多因素中，环境是影响车辆感知决策的最重要因素之一。在本文中，我们提出了一个新的评价标准的神经网络用于无人驾驶汽车的感知模块。环境敏感度（Environmental Sensitivity，ES）是指网络对环境变化的敏感程度。我们设计了一个算法来定量测量不同的感知网络的基础上提取的特征的ES值。实验结果表明，该算法能很好地捕捉网络在不同环境下的敏感度，ES值有助于后续的决策融合过程。
For autonomous driving of unmanned vehicles in intelligent transportation systems, multi-vehicle cooperative perception supported by vehicular networks can greatly improve the accuracy and reliability of the perception decisions. Currently, the perception decisions for a single vehicle are mostly provided by neural networks. Therefore, in order to fuse the perception decisions from multiple vehicles, the credibility of the neural network outputs needs to be studied. Among various factors, the environment is one of the most important affecting vehicles’ perception decisions. In this paper, we propose a new evaluation criteria for the neural networks used in the perception module of unmanned vehicles. This criterion is termed as Environmental Sensitivity (ES), indicates the sensitivity of the network to environmental changes. We design an algorithm to quantitatively measure the ES value of different perception networks based on the extracted features. Experimental results show that our algorithm can well capture the sensitivity of the network in different environments and the ES values will be helpful to the subsequent decision fusion process.