  Merging at highway on-ramps while interacting with other human-driven
vehicles is challenging for autonomous vehicles (AVs). An efficient route to
this challenge requires exploring and exploiting knowledge of the interaction
process from demonstrations by humans. However, it is unclear what information
(or environmental states) is utilized by the human driver to guide their
behavior throughout the whole merging process. This paper provides quantitative
analysis and evaluation of the merging behavior at highway on-ramps with
congested traffic in a volume of time and space. Two types of social
interaction scenarios are considered based on the social preferences of
surrounding vehicles: courteous and rude. The significant levels of
environmental states for characterizing the interactive merging process are
empirically analyzed based on the real-world INTERACTION dataset. Experimental
results reveal two fundamental mechanisms in the merging process: 1) Human
drivers select different states to make sequential decisions at different
moments of task execution, and 2) the social preference of surrounding vehicles
can impact variable selection for making decisions. It implies that efficient
decision-making design should filter out irrelevant information while
considering social preference to achieve comparable human-level performance.
These essential findings shed light on developing new decision-making
approaches for AVs.
