A Sensor-Based Study for Security Events Detection Systems On Board Autonomous Trains

The Human wish for autonomy in vehicles goes back to the 15 th century and has been the subject of numerous research. In the past decade in particular, the rise of Artificial Intelligence & Deep Learning has provided new efficient tools for self-driving transportation systems. Most of existing w...

Full description

Saved in:
Bibliographic Details
Published inInternational Conference on Control, Automation and Systems (Online) pp. 373 - 377
Main Author Nicodeme, Claire
Format Conference Proceeding
LanguageEnglish
Published ICROS 27.11.2022
Subjects
Online AccessGet full text
ISSN2642-3901
DOI10.23919/ICCAS55662.2022.10003694

Cover

More Information
Summary:The Human wish for autonomy in vehicles goes back to the 15 th century and has been the subject of numerous research. In the past decade in particular, the rise of Artificial Intelligence & Deep Learning has provided new efficient tools for self-driving transportation systems. Most of existing works focus on cars, while trains have attracted less attention. However, railway is the most interesting transportation mode in the optic of sustainability. Given the high number of passengers it may carry, an autonomous train must analyze even more accurately its environment. It must also recognize everything happening in its cars to ensure passengers security. The total absence of railway agents on-board fully autonomous trains brings requirements for a monitoring system. It would include sets of sensors for the acquisition, algorithms for analysis and telecommunication network to transfer either the data or its extracted information. Cameras are the first sensor that comes in mind as they would furnish images of the scenes, copying human vision. In addition, other signals such as sound and air composition may supply complementary or new information. The paper offers a review of sensors and their use through the scope of event detection, in the context of public transportation.
ISSN:2642-3901
DOI:10.23919/ICCAS55662.2022.10003694