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Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads

机译:移动的传感器:基于相机的基于相机的实时交通警报为合作道路铺平了道路

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摘要

European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.
机译:近几十年来,欧洲道路安全性大大提高。但是,目前的数字仍然很远,无法到达欧盟委员会的道路安全目标。在这种情况下,通过帮助驾驶员能够做出更好的决策并适应交通状况,建立合作智能运输系统(C-ITS),从而显着提高道路安全,交通效率和驾驶的舒适性。本文提出了两种基于视觉的交通标志识别(TSR)和实时天气警报,例如FOG-BANK。这些模块将支持道路基础架构维护任务以及驱动程序中的运营商,通过C-ITS消息给予它们有价值的信息。使用公共数据集(GTSB)以及我们自己的图像数据库(CEIT-TSR和CEIT-FOGGY)分析不同的最先进的方法。 TSR实现的所选模型基于聚合的Chanel特征(ACF)和卷积神经网络(CNN)实时达到90%以上的精度。关于雾检测,提出了不同颜色空间的图像特征提取方法,以区分阳光,多云和有雾的场景,以及其可见性等级。这两个应用程序已经在车载探头车辆系统中运行。

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