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The unmanned vehicle on-ramp merging model based on AM-MAPPO algorithm.

Zhao Shixin¹, Pan Feng², Jiang Anni¹

¹Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China.

Scientific Reports

|August 21, 2024

Summary

This study introduces an improved autonomous driving merging model using Multi-Agent Proximal Policy Optimization (MAPPO) with an Action-Mask (AM) and noise advantage values, enhancing safety and traffic efficiency.

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Area of Science:

Autonomous Driving Systems
Reinforcement Learning
Robotics

Background:

On-ramp merging presents significant safety and efficiency challenges for autonomous vehicles.
Current autonomous driving systems struggle with low merging success rates and compromised safety in complex scenarios.

Purpose of the Study:

To develop an advanced on-ramp merging model for unmanned vehicles.
To enhance the safety and success rates of autonomous merging maneuvers.
To improve overall traffic flow and efficiency in merging scenarios.

Main Methods:

Implementation of the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm.
Introduction of an Action-Mask (AM) to filter invalid actions and ensure safe merging.
Incorporation of noise advantage values to promote environmental exploration and prevent local optima.

Main Results:

The proposed AM-MAPPO algorithm significantly improves merging safety.
The model demonstrates enhanced traffic efficiency in on-ramp merging scenarios.
Experimental validation confirms the effectiveness of the introduced safety and exploration mechanisms.

Conclusions:

The AM-MAPPO model offers a robust solution for safe and efficient autonomous on-ramp merging.
The integration of Action-Mask and noise advantage values is crucial for overcoming existing limitations.
This research contributes to the advancement of reliable autonomous driving capabilities.