Driving-Over Detection in the Railway Environment

TL;DR

This paper develops and evaluates three methods, including a neural network, for detecting driving-over events in railway environments, achieving high accuracy to support automated train operations.

Contribution

It introduces a neural network-based approach and classical methods for driving-over detection, filling a research gap in railway collision detection technologies.

Findings

Neural network approach achieves 99.6% accuracy.

Classical methods achieve 85% and 88.6% accuracy.

Experiments used diverse objects for realistic testing.

Abstract

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.