Zerrendatu honen arabera: egilea "Zamalloa, Maider"
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Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
Arana-Arexolaleiba, Nestor (IEEE, 2020)The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ... -
Fear Field: Adaptive constraints for safe environment transitions in Shielded Reinforcement Learning
Odriozola Olalde, Haritz; Arana-Arexolaleiba, Nestor (CEUR-WS.org, 2023)Shielding methods for Reinforcement Learning agents show potential for safety-critical industrial applications. However, they still lack robustness on nominal safety, a key property for safety control systems. In the case ... -
Monocular visual odometry for underground railway scenarios
Etxeberria Garcia, Mikel; Labayen, Mikel; Eizaguirre, Fernando; Zamalloa, Maider; Arana-Arexolaleiba, Nestor (SPIE, 2021)In this paper, the application of monocular Visual Odometry (VO) solutions for underground train stopping operation are explored. In order to analyze if the application of monocular VO solutions in challenging environments as ... -
Shielded Reinforcement Learning: A review of reactive methods for safe learning
Arana-Arexolaleiba, Nestor (IEEE, 2023)Reinforcement Learning (RL) algorithms are showing promising results in simulated environments, but their replication in real physical applications, even more so in safety-critical applications, is not yet guaranteed. ... -
Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case
Arana-Arexolaleiba, Nestor (IEEE, 2022)Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the ...
