On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic

TL;DR

This study analyzes human merging behaviors at highway on-ramps in congested traffic, revealing how social preferences influence decision-making and highlighting key environmental states for autonomous vehicle integration.

Contribution

It provides a quantitative analysis of human merging behaviors considering social preferences, identifying critical environmental states and decision mechanisms for AV decision-making.

Findings

Human drivers select different environmental states for decisions at different times.

Social preferences of surrounding vehicles influence decision variables.

Filtering irrelevant information is essential for human-like AV decision-making.

Abstract

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.