When Something Is in the Way: Parameters of Perception and Reaction Speed in Train Drivers

Abstract

A challenge for automation in open track railway systems is the lack of safety standards for obstacle detection and benchmarks for the performance of automated systems. In this work, the foundation for such a benchmark was established with the help of two studies aimed at understanding the reaction time mechanisms of this task. A simulator experiment with professional train drivers and an online study with a larger sample of non-train-drivers were conducted to analyze the reaction time to obstacles along the tracks. The size and contrast of the obstacles, as well as driving speed and use of train protection systems, were varied in a within-subjects design and their effects on reaction time were analyzed with a linear regression model on log-transformed data. The results show that larger obstacles and those with higher contrast are detected significantly faster. Obstacles that are approached at a higher speed were also detected significantly faster. However, varying the train protection system produced ambiguous results. The findings from this research provide a baseline for further research on train driver sensory capabilities and safety standard definition for future automation.

Keywords: Train Drivers, ATO-Systems, Human as Benchmark

DOI: 10.54941/ahfe1004139