Automated Lane Merging via Game Theory and Branch Model Predictive Control

TL;DR

This paper presents an integrated planning framework for automated lane merging that combines game theory with branch model predictive control to handle vehicle interactions and uncertainties in dense traffic.

Contribution

It introduces a novel combination of game-theoretic behavior modeling with branch MPC for lane merging, including a tailored solver for efficiency.

Findings

Effective handling of vehicle interactions in dense traffic

Validation with real traffic data shows improved merging performance

The framework accounts for strategic behaviors like Nash and Stackelberg strategies

Abstract

We propose an integrated behavior and motion planning framework for the lane-merging problem. The behavior planner combines search-based planning with game theory to model vehicle interactions and plan multi-vehicle trajectories. Inspired by human drivers, we model the lane-merging problem as a gap selection process and determine the appropriate gap by solving a matrix game. Moreover, we introduce a branch model predictive control (BMPC) framework to account for the uncertain equilibrium strategies adopted by the surrounding vehicles, including Nash and Stackelberg strategies. A tailored numerical solver is developed to enhance computational efficiency by exploiting the tree structure inherent in BMPC. Finally, we validate our proposed integrated planner using real traffic data and demonstrate its effectiveness in handling interactions in dense traffic scenarios. The code is publicly available at: https://github.com/SailorBrandon/GT-BMPC.