Automated Vehicle Highway Merging: Motion Planning via Adaptive Interactive Mixed-Integer MPC

TL;DR

This paper introduces aiMPC, an adaptive mixed-integer MPC framework for automated highway merging that predicts and plans vehicle motion considering neighboring vehicles, improving safety and efficiency.

Contribution

The paper presents a novel adaptive interactive mixed-integer MPC approach that jointly optimizes merging maneuvers and predicts neighboring vehicle behavior.

Findings

Effective in simulation for highway merging scenarios

Handles non-convex feasible regions with mixed-integer formulation

Adapts to neighboring vehicle behavior using inverse optimal control

Abstract

A new motion planning framework for automated highway merging is presented in this paper. To plan the merge and predict the motion of the neighboring vehicle, the ego automated vehicle solves a joint optimization of both vehicle costs over a receding horizon. The non-convex nature of feasible regions and lane discipline is handled by introducing integer decision variables resulting in a mixed integer quadratic programming (MIQP) formulation of the model predictive control (MPC) problem. Furthermore, the ego uses an inverse optimal control approach to impute the weights of neighboring vehicle cost by observing the neighbor's recent motion and adapts its solution accordingly. We call this adaptive interactive mixed integer MPC (aiMPC). Simulation results show the effectiveness of the proposed framework.