Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911185631182848 |
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| author | Liu, Qi Li, Xueyuan Li, Zirui Gim, Juhui |
| author_facet | Liu, Qi Li, Xueyuan Li, Zirui Gim, Juhui |
| contents | Navigating heterogeneous traffic environments with diverse driving styles poses a significant challenge for autonomous vehicles (AVs) due to their inherent complexity and dynamic interactions. This paper addresses this challenge by proposing a heterogeneous graph reinforcement learning (GRL) framework enhanced with an expert system to improve AV decision-making performance. Initially, a heterogeneous graph representation is introduced to capture the intricate interactions among vehicles. Then, a heterogeneous graph neural network with an expert model (HGNN-EM) is proposed to effectively encode diverse vehicle features and produce driving instructions informed by domain-specific knowledge. Moreover, the double deep Q-learning (DDQN) algorithm is utilized to train the decision-making model. A case study on a typical four-way intersection, involving various driving styles of human vehicles (HVs), demonstrates that the proposed method has superior performance over several baselines regarding safety, efficiency, stability, and convergence rate, all while maintaining favorable real-time performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach Liu, Qi Li, Xueyuan Li, Zirui Gim, Juhui Artificial Intelligence Navigating heterogeneous traffic environments with diverse driving styles poses a significant challenge for autonomous vehicles (AVs) due to their inherent complexity and dynamic interactions. This paper addresses this challenge by proposing a heterogeneous graph reinforcement learning (GRL) framework enhanced with an expert system to improve AV decision-making performance. Initially, a heterogeneous graph representation is introduced to capture the intricate interactions among vehicles. Then, a heterogeneous graph neural network with an expert model (HGNN-EM) is proposed to effectively encode diverse vehicle features and produce driving instructions informed by domain-specific knowledge. Moreover, the double deep Q-learning (DDQN) algorithm is utilized to train the decision-making model. A case study on a typical four-way intersection, involving various driving styles of human vehicles (HVs), demonstrates that the proposed method has superior performance over several baselines regarding safety, efficiency, stability, and convergence rate, all while maintaining favorable real-time performance. |
| title | Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25751 |