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Related Experiment Video

Updated: Jun 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

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Published on: February 1, 2020

Mandatory lane-changing decision and control method based on game theory.

Zhe Wang^1,2, Xinyi Zhang¹, Haoze Ren¹

¹College of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan, China.

|June 12, 2026

Summary

This summary is machine-generated.

This study introduces a new framework for autonomous vehicle lane changes in mandatory scenarios using game theory. It improves trajectory tracking and adapts driving strategies based on surrounding vehicle interactions.

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Area of Science:

Autonomous Driving Systems
Game Theory in Robotics
Vehicle Dynamics and Control

Background:

Mandatory lane-changing scenarios pose significant challenges for autonomous vehicles (AVs) due to complex interactions with surrounding traffic.
Existing frameworks often struggle to dynamically adapt to the interactive nature of human-driven vehicles during lane changes.

Purpose of the Study:

To develop and evaluate a novel lane-changing decision and control framework for AVs in mandatory lane-changing situations.
To enhance the safety and efficiency of AVs by enabling adaptive trajectory planning and robust control.
To investigate the influence of driver behavior on the performance of the proposed framework.

Main Methods:

A stage game decision-making approach was employed, discretizing the lane-changing process and constructing payoff functions for interacting vehicles.
A composite error metric was defined for trajectory tracking, utilizing a sliding mode controller for robust performance.
A joint simulation platform integrating traffic simulation and driver-in-the-loop experiments was developed for validation.

Main Results:

The proposed framework demonstrated adaptive adjustment of driving strategies and planned trajectories based on real-time vehicle interactions.
The sliding mode controller ensured robust trajectory tracking performance by managing lateral and heading angle deviations.
Preliminary driver-in-the-loop experiments provided initial insights into the framework's effectiveness under varying driving tendencies.

Conclusions:

The developed lane-changing framework effectively manages complex interactions in mandatory scenarios, enhancing autonomous vehicle maneuverability.
The integration of game theory and robust control provides a promising approach for safe and efficient autonomous driving.
Further research with diverse driving behaviors is recommended to fully validate the framework's real-world applicability.