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.

翻译：为实现列车全自动驾驶，铁路系统需引入众多新技术组件。目前由运营人员执行的任务需由自动化系统接管，因此需要自动列车运行及环境监测设备。其中，碰撞检测是关键任务，包括（1）列车前端碰撞与（2）车轮碾压事件。当前针对碾压事件的检测技术研究尚不充分。为此，本研究使用钢、木、石、骨等多种材质物体开展了详细碾压实验，为全自动铁路运营积累数据。基于采集的测试数据，开发了三种自动检测碾压事件的方法：第一种基于卷积神经网络，另两种为经典阈值方法。神经网络方法的平均准确率达99.6%，而两种经典方法分别达到85%和88.6%。