Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors

TL;DR

This paper presents a decision-making framework for autonomous vehicles in forced merging scenarios that models social behaviors and personal objectives, using Bayesian filtering and receding-horizon control to predict and respond to driver intentions.

Contribution

It introduces a novel interaction-aware decision-making approach that integrates social psychology factors with Bayesian filtering for improved autonomous vehicle behavior in complex traffic.

Findings

Outperforms game theoretic controllers in simulations

Effective in predicting driver intentions under uncertainty

Validated with real-world traffic data

Abstract

Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging. In this paper, we consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. Leveraging this model, we develop a receding-horizon control-based decision-making strategy, that estimates online the other drivers' intentions using Bayesian filtering and incorporates predictions of nearby vehicles' behaviors under uncertain intentions. The effectiveness of the proposed decision-making strategy is demonstrated and evaluated based on simulation studies in comparison with a game theoretic controller and a real-world traffic dataset.