The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning
Open accessAbstract
Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes decision-making a challenging task in an unsignalized intersection scenario. In this paper, a decentralized multi-agent proximal policy optimization (MAPPO) based on an attention representations algorithm (Attn-MAPPO) was developed to make joint decisions at an intersection to avoid collisions and cross the intersection effectively. To implement this framework, by exploiting the shared information, the system was modeled as a model-free, fully cooperative, multi-agent system. The vehicle employed an attention module to extract the most valuable information from its neighbors. Based on the observation and traffic rules, a joint policy was identified to work more cooperatively based on the trajectory prediction of all the vehicles. To facilitate the collaboration between the vehicles, a weighted reward assignment scheme was proposed to focus more on the vehicles approaching intersections. The results presented the advantages of the Attn-MAPPO framework and validated the effectiveness of the designed reward function. Ultimately, the comparative experiments were conducted to demonstrate that the proposed approach was more adaptive and generalized than the heuristic rule-based model, which revealed its great potential for reinforcement learning in the decision-making of autonomous driving.
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“The major drawback of the previous study is that it focuses solely on traffic efficiency metrics like fuel consumption and travel time, limiting its applicability in real-time, largescale 6G-enabled VANET environments.”
From Multi-agent Reinforcement Learning for Real-time Traffic Control in 6G-Enabled VANET Environments (2025) · cites this paper as [17]
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Cited by
Sorted by citations
Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
Fang, Z., Ma, T., Huang, J., Niu, Z., & Yang, F. (2025) · Applied Sciences · doi:10.3390/app15041905 · Cited by 19
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Group-benefit control strategy for connected automated vehicles in mixed traffic at intersections
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Metadata
- Authors
- Zhuang, H., Lei, C., Chen, Y., & Tan, X.
- Affiliations
- Sun Yat-sen University (CN)
- Corresponding author
- Tan, Xiaojun
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2023-04-17
- Volume
- 13
- Issue
- 8
- Pages
- 5018
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4366180250
- Cited by
- 22
- References
- 27
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 7
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Key-Area Research and Development Program of Guangdong Province (2020B0909030005, 2020B090921003)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- mixed traffic, unsignalized intersections, cooperative decision-making, multi-agent reinforcement learning, proximal policy optimization, attention mechanism, connected and automated vehicles, human-driven vehicles, collision avoidance, trajectory prediction, reward function design, autonomous driving
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