Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors
This addresses the challenge of safe and efficient autonomous driving in complex traffic situations like highway merging, though it appears incremental by building on existing behavioral models and control methods.
The paper tackles the problem of autonomous vehicle decision-making in forced merging scenarios by developing a control strategy that estimates other drivers' intentions using Bayesian filtering and incorporates predictions of their behaviors. The method was evaluated through simulations against a game theoretic controller and a real-world dataset, showing effectiveness but without specific numerical results.
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.