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An Image-Based Approach for Classification of Driving Behaviour Using CNNs

机译:一种基于图像的方法,用于使用CNNS分类驾驶行为

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In this work we present an approach for the classification of driving behaviour using Convolutional Neural Networks (CNNs), based on measurements that have been obtained by the internal CAN-bus of the vehicle. As is the case with different driving behaviours, CAN-bus sensor data reflect the driving patterns associated with different types of vehicles. The experimental evaluation is performed on a real-life dataset composed by measuring 27 attributes, for 4 different car types, namely vacuum, car, truck and garbage truck. These features are processed to form pseudocolored images, capturing both temporal and qualitative features of parts of routes. For classification, we use a deep CNN architecture. Results indicated an accuracy of 91% and increased performance compared to other neural network-based approaches.
机译:在这项工作中,我们向使用卷积神经网络(CNNS)的驾驶行为进行分类的方法,基于已经通过车辆的内部帆船总线获得的测量。 与不同的驾驶行为一样,CAN总线传感器数据反映与不同类型的车辆相关联的驱动图案。 实验评估在通过测量27个属性组成的现实生活数据集上进行,适用于4种不同的汽车类型,即真空,汽车,卡车和垃圾车。 处理这些功能以形成伪彩色图像,捕获部分路由的时间和定性特征。 对于分类,我们使用深入的CNN架构。 结果表明,与其他基于神经网络的方法相比,精度为91%和性能增加。

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