Impact of Automation at Different Cognitive Stages on High-Speed Train Driving Performance

In the process of intelligent driving technology development, the performance of human–machine systems for high-speed train driving tasks has been scrutinized and challenged. The human cognitive process can be divided into several stages. Within each of these stages, automation can be applied across different levels. However, the impact of different automation levels at different cognitive stages on high-speed train driving performance is still unclear. In this study, different level of automation (LOA) of high-speed train driving functions at different cognitive stages are designed, and 30 participants are recruited to execute orthogonal Latin square experiments. The experimental results show that driving automation in the decision-making stage significantly improved performance in a speed control task but also increased the mental workload and reaction time for monitoring train signals. However, this effect did not occur in a track obstacle observation response time. These results show that driving automation not only improves the performance of primary driving tasks but also has a negative impact on secondary driving-related tasks. This study evaluates the influence of high-speed train driving automation on the performance of human–machine systems and analyzes the advantages and costs of different LOA at different cognitive stages. It provides a theoretical basis for the design of intelligent high-speed train driving systems from the perspective of human–machine collaboration.