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Visibility estimation of traffic signals under rainy weather conditions for smart driving support

机译:雨天条件下交通信号的可视性估计,可提供智能驾驶支持

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The aim of this work is to support a driver by notifying the information of traffic signals in accordance with their visibility. To avoid traffic accidents, the driver should detect and recognize surrounding objects, especially traffic signals. However, when driving a vehicle under rainy weather conditions, it is difficult for drivers to detect or to recognize objects existing in the road environment in comparison with fine weather conditions. Therefore, this paper proposes a method for estimating the visibility of traffic signals for drivers under rainy weather conditions by image processing. The proposed method is based on the concept of visual noise known in the field of cognitive science, and extracts two types of visual noise features which ware considered that they affect the visibility of traffic signals. We expect to improve the accuracy of visibility estimation by combining the visual noise features with the texture feature introduced in a previous work. Experimental results showed that the proposed method could estimate the visibility of traffic signals more accurately under rainy weather conditions.
机译:这项工作的目的是通过根据交通信号的可视性通知交通信号信息来为驾驶员提供支持。为避免交通事故,驾驶员应检测并识别周围物体,尤其是交通信号灯。然而,当在多雨的天气条件下驾驶车辆时,与晴朗的天气条件相比,驾驶员难以检测或识别道路环境中存在的物体。因此,本文提出了一种通过图像处理估计驾驶员在雨天条件下交通信号可视性的方法。所提出的方法基于认知科学领域中已知的视觉噪声的概念,并且提取两种类型的视觉噪声特征,其被认为影响交通信号的可见性。我们希望通过将视觉噪声特征与先前工作中介绍的纹理特征相结合来提高可见性估计的准确性。实验结果表明,该方法能够在雨天条件下更准确地估计交通信号的可见度。

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