Abstract
Autonomous driving tends to increase use of perception as a tool for analyzing the environment before making a decision that could impact driving. However, recent techniques based on machine learning do not provide the necessary interpretability to ensure sufficient driving safety. Combining multiple sources, deterministic or not, allows results to be cross-referenced and therefore more reliable. In this paper, we propose a novel methodology that aligns an infrastructure mapping system and point cloud analysis for railway tracks and catenaries perception to ensure autonomous train’s safety. By using a deep learning model to recognize and classify rails with the implicit knowledge of the railway infrastructure, we exceed in performance all previous systems of infrastructure: 60.9% in mIoU for tracks segmentation and 9.27 points mMink for points alignment with ground-truth, at an interesting runtime of 20 Hz. Moreover, we propose an embedded solution for automatic monitoring which avoids hours of maintenance traffic on the railway tracks. This solution is used as acquisition system feeding map and perception in real-world data for autonomous trains.
This research work is funded by the French program “Investissements d’Avenir” and is part of the French collaborative project TASV (Train Autonome Service Voyageurs), with SNCF, Alstom Crespin, Thales, Bosch, and Spirops.
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Mahtani, A., Chouchani, N., Herbreteau, M., Rafin, D. (2022). Enhancing Autonomous Train Safety Through A Priori-Map Based Perception. In: Collart-Dutilleul, S., Haxthausen, A.E., Lecomte, T. (eds) Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. RSSRail 2022. Lecture Notes in Computer Science, vol 13294. Springer, Cham. https://doi.org/10.1007/978-3-031-05814-1_8
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