Identity verification and proctoring of online students are one of the key challenges to online learning today. Especially for online certification and accreditation, the training organizations need to verify that the online students who completed the learning process and received the academic credits are those who registered for the courses. Furthermore, they need to ensure that these students complete all the activities of online training without cheating or inappropriate behaviours. The COVID-19 pandemic has accelerated (abruptly in certain cases) the migration and implementation of online education strategies and consequently the need for safe mechanisms to authenticate and proctor online students. Nowadays, there are several technologies with different grades of automation. In this paper, we deeply describe a specific solution based on the authentication of different biometric technologies and an automatic proctoring system (system workflow as well as AI algorithms), which incorporates features to solve the main concerns in the market: highly scalable, automatic, affordable, with few hardware and software requirements for the user, reliable and passive for the student. Finally, the technological performance test of the large scale system, the usability-privacy perception survey of the user and their results are discussed in this work.

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 last years. However, VO algorithms are usually evaluated in outdoor street scenarios and do not consider underground railway scenarios, with low lighting conditions in tunnels and significant lighting changes between tunnels and railway platforms. Besides, there is a lack of GPS, and it is not easy to access such infrastructures. This research proposes a method to create a ground truth of images and poses in underground railway scenarios. Second, the EnlightenGAN algorithm is proposed to face challenging lighting conditions, which can be coupled with any state-of-the-art VO techniques. Finally, the obtained ground truth and the EnlightenGAN have been tested in a real scenario. Two different VO approaches have been used: ORB-SLAM2 and DF-VO. The results show that the EnlightenGAN enhancement improves the performance of both approaches.

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.

The automation of railway operations is an activity in constant growth. Different railway stakeholders are already developing their research activities for the future driverless autonomous driving based on Computer Vision (CV) and Artificial Intelligence (AI) enhanced perception technologies (e.g., obstacle detection). Unfortunately, the AI models are opaque in nature and here is no certification accepted rules for CV&AI-enhanced functionality certification. Capturing and labelling camera image in real environment is expensive in terms of time and resources and it does not guarantee enough variation in edge visibility conditions, which makes resulting database less valuable for the V&V processes. To meet the increasing needs of trusted CV&AI-based solutions, numerous Validation and Verification (V&V) approaches have been proposed in other sectors like automotive, most of the based on virtual simulators. Unfortunately, there is currently no virtual perception simulator for railway scenario. This work aims to create an semi-automatic system based on virtual scenarios measuring the CV&AI-enhanced system performance facing different visibility conditions. It will be based on the global accuracy metrics and detected potential safety and operation rules violations. This work also demonstrates the quantitative and qualitative improvements while reducing current V&V cost.