# MAPPO-PIS: A Multi-Agent Proximal Policy Optimization Method with Prior Intent Sharing for CAVs' Cooperative Decision-Making

arXiv: https://arxiv.org/abs/2408.06656 · HTML: https://mlsanity.com/paper/2408.06656/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.24152/) (2024)
- [Safe Heterogeneous Multi-Agent RL with Communication Regularization for Coordinated Target Acquisition](https://mlsanity.com/paper/2601.08327/) (2026)
- [Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles](https://mlsanity.com/paper/2604.07378/) (2026)
- [Uncertainty-Aware Motion Planning for Autonomous Driving in Mixed Traffic Environment](https://mlsanity.com/paper/2606.09958/) (2026)

## Abstract (arXiv)

Vehicle-to-Vehicle (V2V) technologies have great potential for enhancing traffic flow efficiency and safety. However, cooperative decision-making in multi-agent systems, particularly in complex human-machine mixed merging areas, remains challenging for connected and autonomous vehicles (CAVs). Intent sharing, a key aspect of human coordination, may offer an effective solution to these decision-making problems, but its application in CAVs is under-explored. This paper presents an intent-sharing-based cooperative method, the Multi-Agent Proximal Policy Optimization with Prior Intent Sharing (MAPPO-PIS), which models the CAV cooperative decision-making problem as a Multi-Agent Reinforcement Learning (MARL) problem. It involves training and updating the agents' policies through the integration of two key modules: the Intention Generator Module (IGM) and the Safety Enhanced Module (SEM). The IGM is specifically crafted to generate and disseminate CAVs' intended trajectories spanning multiple future time-steps. On the other hand, the SEM serves a crucial role in assessing the safety of the decisions made and rectifying them if necessary. Merging area with human-machine mixed traffic flow is selected to validate our method. Results show that MAPPO-PIS significantly improves decision-making performance in multi-agent systems, surpassing state-of-the-art baselines in safety, efficiency, and overall traffic system performance. The code and video demo can be found at: urlhttps://github.com/CCCC1dhcgd/A-MAPPO-PIS.