# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 1 Introduction  
Users of the European rail industry are clamouring for a future Automatic Train Operation (ATO) system since it provides advantages such as lower operating costs, longer product life-cycles for railways and increased safety. Its definition is being worked on by the European Shift2 Rail standards group [1]. For fully autonomous train operation, various rolling stock suppliers and stakeholders have already begun researching, developing and testing technologies.  
Similarly to other transport sectors, different computational issues are being faced by numerous railway suppliers and stakeholders for CV- and AI-enhanced autonomous train operation. The adoption of computer equipment capable of offering the performance of high-end graphic-processor units while being able to simultaneously meet safety criteria will be necessary for the future of CV and AI advances in the railway sector. These developments will increase the size, speed, and dependability of CV and AI processing calculations.  
Through the use of multi-cores, Graphic Processors Units (GPUs), and specialized accelerators, a number of HPC commercial off-the-shelf platforms provide the calculation capabilities required by autonomous systems in fields such as intelligent transportation systems, space, and robotics [2].  
However, because of the challenges or barriers that HPC platforms pose to the certification process, such as support for functional and timing isolation and testability, the use of these platforms has historically been viewed as being beyond the reach of the industry of safety-critical systems (i.e., controllability and observability). Therefore, the state-of-the-art (SoA) safety-critical computing platforms cannot currently satisfy these demanding specifications.  
The SELENE (Self-monitored Dependable Platform for High-Performance Safety-Critical Systems) project [3] is a European R&D initiative that develops the research provided in this article as a use case demonstration. This work proposes a high-performance platform with safety-related considerations as a main design goal in an effort to bridge this gap. The SELENE platform is an open-source Reduced Instruction Set Computer V (RISC-V) [4] multi-core processor with hardware acceleration for artificial intelligence that supports multiple types of redundancy, real-time performance monitoring, and enforcement mechanisms to ensure that the safety objectives of the applications are satisfied. Additionally, the design of this system-on-open-source chip makes it possible for it to easily adapt to other safety domains. Apart from the use case presented in this article, three other use cases from the automotive and space industries have been used to test this method.  
The article is organized as follows: Section 2 gives an overview of some relevant related works and highlights the main differences with our approach. Section 3 describes the railway domain use case in which the approach is being tested. Section 4 presents the use case deployment details analysing the new HW and SW modules included in the platform and the platform architecture, taking into account the safety-related analysis of our use case. Sections 5 and 6 define the test which was carried out and presents the obtained results in order to demonstrate the performance of the solution. Finally, Section 7 presents the conclusions and future work.