--Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents an online behavior-based monitoring system utilizing a cus-tomised Directed-Graph Neural Network (DGNN) to classify train driver's states into three categories: alert, not alert, and pathological. T o optimize input representations for the model, an ablation study was performed, comparing three feature configurations: skeletal-only, facial-only, and a combination of both. Experimental results show that combining facial and skeletal features yields the highest accuracy (80.88%) in the three-class model, outperforming models using only facial or skeletal features. Furthermore, this combination achieves over 99% accuracy in the binary alertness classification. Additionally, we introduced a novel dataset that, for the first time, incorporates simulated pathological conditions into train driver monitoring, broadening the scope for assessing risks related to fatigue and health. This work represents a step forward in enhancing railway safety through advanced online monitoring using vision-based technologies. Fatigue is a critical safety concern in railway operations, where long shifts and repetitive activities can significantly impair a driver's alertness [1]. Despite regulations aimed at ensuring adequate rest for train drivers, fatigue-related incidents remain alarmingly common. According to research by the AA Charitable Trust, one in eight drivers admits to falling asleep at the wheel, while nearly two-fifths have felt so tired that they feared they might nod off [2]. The risk is particularly high with modern automated trains operating on night shifts and monotonous routes [3].

Submission Date: January 14, 2025 Y. C. Luo, J. Xun, W. Wang, R. Z. Zhang, Z. C. Zhao 2 ABSTRACT With the rapid development of China high-speed railway, drivers face increasingly significant technical challenges during operations, such as fault handling. Currently, drivers depend on the onboard mechanic when facing technical issues, for instance, traction loss or sensor faults. This dependency can hinder effective operation, even lead to accidents, while waiting for faults to be addressed. To enhance the accuracy and explainability of actions during fault handling, an Intelligent Driver Advisory System (IDAS) framework based on a large language model (LLM) named IDAS-LLM, is introduced. Initially, domain-fine-tuning of the LLM is performed using a constructed railway knowledge question-and-answer dataset to improve answer accuracy in railway-related questions. Subsequently, integration of the Retrieval-augmented Generation (RAG) architecture is pursued for system design to enhance the explainability of generated responses. Comparative experiments are conducted using the constructed railway driving knowledge assessment dataset. Results indicate that domain-fine-tuned LLMs show an improvement in answer accuracy by an average of 10%, outperforming some current mainstream LLMs. Additionally, the inclusion of the RAG framework increases the average recall rate of question-and-answer sessions by about 4%. Finally, the fault handling capability of IDAS-LLM is demonstrated through simulations of real operational scenarios, proving that the proposed framework has practical application prospects.

When trains collide with obstacles, the consequences are often severe. To assess how artificial intelligence might contribute to avoiding collisions, we need to understand how train drivers do it. What aspects of a situation do they consider when evaluating the risk of collision? In the present study, we assumed that train drivers do not only identify potential obstacles but interpret what they see in order to anticipate how the situation might unfold. However, to date it is unclear how exactly this is accomplished. Therefore, we assessed which cues train drivers use and what inferences they make. To this end, image-based expert interviews were conducted with 33 train drivers. Participants saw images with potential obstacles, rated the risk of collision, and explained their evaluation. Moreover, they were asked how the situation would need to change to decrease or increase collision risk. From their verbal reports, we extracted concepts about the potential obstacles, contexts, or consequences, and assigned these concepts to various categories (e.g., people's identity, location, movement, action, physical features, and mental states). The results revealed that especially for people, train drivers reason about their actions and mental states, and draw relations between concepts to make further inferences. These inferences systematically differ between situations. Our findings emphasise the need to understand train drivers' risk evaluation processes when aiming to enhance the safety of both human and automatic train operation.

The cliche about video games is that they're all about escapism. When people switch on a PlayStation or souped-up PC, they do it to lose themselves in a mythical world or intergalactic conflict. They do not come here to power wash a patio. But increasingly, that orthodoxy is being tested. The surging success of the job simulator, in which players take on seemingly mundane real-world careers, shows that our relationship with games is a lot more complex.

A level crossing is an important but often dangerous component of rail infrastructure. To rectify the situation, Slovakian IT company GrowthPro has developed Erriate, an AI-based solution that is able to prevent the accidents at level crossings. A lot of rail infrastructure managers spend huge amounts to make level crossings safer for both car and train drivers, pedestrians and train passengers. Usually, they install barriers, signalling equipment or even video cameras. The digital solutions could be a good supplement to these moves and improve safety at the level crossings.

Trains with a guard become driver-only trains, which then become driverless trains. That's the fear underlying Aslef's dispute with Southern railways and accounts for the rearguard action to prevent further job losses across the rail industry. There is also scorn for Southern's management, which has attacked drivers' basic terms and conditions, and there is anger at transport secretary Chris Grayling's anti-union stance. But, at its heart, the dispute is over the status and even the very existence of the job of train driver, which has been around for nigh on 200 years. Like most people, train drivers will have read the screaming headlines warning of a robot revolution that spells the end for millions of jobs.