An Efficient Game-Theoretic Planner for Automated Lane Merging with Multi-Modal Behavior Understanding

Conference paper (2023)

Authors

L. Zhang Team Sergio Grammatico - Mechanical, Maritime and Materials Engineering
S. Han Student
S. Grammatico Team Sergio Grammatico - Mechanical, Maritime and Materials Engineering ,
Research Group
Team Sergio Grammatico (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Sergio Grammatico

Abstract

In this paper, we propose a novel behavior planner that combines game
theory with search-based planning for automated lane merging.
Specifically, inspired by human drivers, we model the interaction
between vehicles as a gap selection process. To overcome the challenge
of multi-modal behavior exhibited by the surrounding vehicles, we
formulate the trajectory selection as a matrix game and compute an
equilibrium. Next, we validate our proposed planner in the high-fidelity
simulator CARLA and demonstrate its effectiveness in handling
interactions in dense traffic scenarios.

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