提出三种铁路驾驶过检测方法，神经网络准确率达99.6%。
Driving-Over Detection in the Railway Environment
- 用卷积神经网络与两种阈值法检测列车碾压事件
- 神经网络方法平均准确率99.6%，优于传统方法
- 基于真实碾压实验数据，适合自动化列车系统
为实现列车完全自动化运行，需引入多项新技术组件，将原由工作人员完成的任务交由自动系统处理。其中，环境感知与碰撞检测至关重要，尤其包括列车前方碰撞及车轮碾压事件（即驾驶过）。当前对驾驶过事件的检测技术研究较少。为此，开展了详细的驾驶过实验，使用钢、木、石和骨等材料制成的物体进行测试，采集了大量数据。基于这些数据，开发了三种自动检测方法：一种基于卷积神经网络，另两种为经典阈值法。神经网络方法平均准确率达99.6%，两种阈值法分别达到85%和88.6%。结果表明，深度学习方法在驾驶过事件检测中具有显著优势。
原文摘要 · Abstract (English)
To enable fully automated driving of trains, numerous new technological components must be introduced into the railway system. Tasks that are nowadays carried out by the operating stuff, need to be taken over by automatic systems. Therefore, equipment for automatic train operation and observing the environment is needed. Here, an important task is the detection of collisions, including both (1) collisions with the front of the train as well as (2) collisions with the wheel, corresponding to an driving-over event. Technologies for detecting the driving-over events are barely investigated nowadays. Therefore, detailed driving-over experiments were performed to gather knowledge for fully automated rail operations, using a variety of objects made from steel, wood, stone and bones. Based on the captured test data, three methods were developed to detect driving-over events automatically. The first method is based on convolutional neural networks and the other two methods are classical threshold-based approaches. The neural network based approach provides an mean accuracy of 99.6% while the classical approaches show 85% and 88.6%, respectively.
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