HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
-
Published:2023-08-07
Issue:15
Volume:13
Page:9017
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Labayen Mikel12ORCID, Medina Laura3, Eizaguirre Fernando4ORCID, Flich José3ORCID, Aginako Naiara2ORCID
Affiliation:
1. Autonomous Vehicle Department, CAF Signalling, 20018 Donostia, Spain 2. Computer Sciences and Artificial Intelligence Department, University of the Basque Country, 20018 Donostia, Spain 3. Computer Engineering Department, Universitat Politècnica de València, 46022 Valencia, Spain 4. Embedded Systems Department, Ikerlan Technology Research Centre, 20500 Arrasate/Mondragón, Spain
Abstract
The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference33 articles.
1. (2023, May 05). Shift2Rail—Home. Available online: https://shift2rail.org/. 2. Reddi, V.J.e.a. (2019). MLPerf Inference Benchmark. arXiv. 3. (2023, May 05). CORDIS—SELENE. Available online: https://cordis.europa.eu/project/id/871467/en. 4. Waterman, A., Lee, Y., Patterson, D.A., and Asanović, K. (2014). The RISC-V Instruction Set Manual, Volume I: User-Level ISA, Version 2.0., EECS Department, University of California. Technical Report UCB/EECS-2014-54. 5. Palmer, A.W., Sema, A., Martens, W., Rudolph, P., and Waizenegger, W. (2020, January 20–23). The Autonomous Siemens Tram. Proceedings of the IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.
|
|