Work

Obstacle detection on rail-tracks: Study on situations for requirement specification

Year: 2023

Type: article

Abstract: With the advance in technologies that enable assisted and driverless operations, developing detection systems that can detect any obstacle within a train's clearance gauge is critical. As a first step, it is vital to understand the hazardous track situations resulting in railway accidents. Currently, to the best of our knowledge, no public railway databases that document accident situations focus on obstacles on or near the tracks. In this study, we collect railway accident reports from different sources and establish a database2 of collisions and near-misses, including descriptions of the obstacle involved. Furthermore, our analysis provides a comprehensive list of obstacle classes and critical scenarios to consider during the development and testing of obstacle detection frameworks for a railway system.

Source: Transportation research procedia

DOI: 10.1016/j.trpro.2023.11.498

Authors: Fabian Hampel, Philipp Leibner, Harish Manjunatheswaran, Akke Schotten, Christian Schindler

Institution: RWTH Aachen University

Language: English

Topic: Transportation Systems and Safety

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Bundesministerium für Verkehr und Digitale Infrastruktur