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ISSN 2753-3239
ISSN 2753-3239
CCC: 7
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Paper 11.9
Analysing Railway Accidents: A Statistical Approach to Evaluating Human Performance in Obstacle Detection
O. Lahneche1, A. Haag2, P. Dendorfer2, V. Aravantinos2, M. Guilbert2 and M. Sallak1
1Laboratoire Heudiasyc, Université de Technologie de Compiègne, Compiègne, France
2Research and Development Unit, Futurail, Munich, Germany
Full Bibliographic Reference for this paper
O. Lahneche, A. Haag, P. Dendorfer, V. Aravantinos, M. Guilbert, M. Sallak, "Analysing Railway Accidents: A Statistical Approach to Evaluating Human Performance in Obstacle Detection", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on
Railway Technology: Research, Development and Maintenance",
Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 7, Paper 11.9, 2024, doi:10.4203/ccc.7.11.9
Keywords: obstacle detection, train driver performance, safety, railways, autonomous train, reference system.
Abstract
This paper evaluates human driver performance in obstacle detection on railway systems by statistically analysing accident data. Given the challenge of obtaining non-accident data, the study estimates obstacle frequency on tracks by examining accident rates under low visibility conditions (night and curves), where drivers cannot see obstacles in time.
Using data from a major European railway operator, the study finds that human drivers can avoid around 28% of collisions in good visibility conditions, but only about 12% when all conditions are considered. Autonomous trains need to meet at least an equivalent performance level. This research aims to support the safety certification of autonomous train systems by offering a benchmark of human performance in obstacle detection, emphasising the need for comprehensive data and rigorous validation of hypotheses regarding driver reactions and environmental conditions.
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