Exploring Human and Artificial Attention Mechanisms in Driving Scenarios

Understanding attention is crucial for improving safety in driving scenarios. Detected and classified objects, along with their observation by the driver, are used as a measure of attention. This paper investigates the differences between human and artificial attention in real-world and replay driving scenarios. By analyzing attention patterns from drivers and a vision-language model agent, we identify a number of differences. The results highlight the limitations of current AI attention models and suggest the way forward for developing more context-aware systems.