Assessment of adaptive algorithms for automatic control of train braking system

Potapovs, Andrejs; Levchenkov, Anatoly; Gorobetz, Mikhail

doi:10.1109/rtucon.2014.6998216

Cited by 1 publication

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“…Many railways use a train control system meaning Continuous Automatic Train Signalling (ALSN) or a similar system, whose main aim is to transmit the indications of the traffic lights to the locomotives driver's cab. By contrast, the authors of the present research propose to make traffic light and other wagon detection using CNN and its signal recognition in real time, as well as to use this information for the locomotive braking system control, instead of informing the driver [11], [12]. This procedure may help minimize a human factor and decrease the necessary time for the reaction.…”

Section: Problem Formulationmentioning

confidence: 99%

Algorithms for Railway Embedded Control Devices for Safety Manoeuvres

Beinarovica

Gorobetz

Alps

2020

Electrical, Control and Communication Engineering

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This study is dedicated to solve manoeuvres making task while working on the station with no marshalling hump. It is part of the project aimed at the development of intelligent safety and optimal control systems of autonomous electric vehicles and transport in general. The main manoeuvres safety depends on the lack of items and other objects on the rails as well as on the position of turnouts. In most cases rails, occupied with other wagons, as well as the wrong position of turnouts are marked with prohibiting red or blue signals of the traffic light. The authors propose an algorithm for the traffic light recognition by using a convolutional neural network (CNN) and traffic light indicator recognition. However, the situation when the locomotive needs to drive on the rails occupied with other wagons, for example, during the manoeuvres on the railway station can also appear. For this purpose, the authors have developed a CNN algorithm for the wagon recognition on the rails.

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Section: Problem Formulationmentioning

confidence: 99%