mlsanityData to 7 Oct 2026

Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach

arXiv 2509.25751 ↗First posted 30 Sep 2025cs.AIcited by 0

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 8 of its 22 references that are in the map. First posted on arXiv 30 Sep 2025.

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8 of 22 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach5 May

Other references · 7

  1. 2017Graph Attention Networkscited by 8,854
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2021Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlookcited by 14
  4. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networkscited by 13
  5. 2023Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimationcited by 10
  6. 2021r-GAT: Relational Graph Attention Network for Multi-Relational Graphscited by 9
  7. 2024Lane-Change in Dense Traffic with Model Predictive Control and Neural Networkscited by 5

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  3. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
  4. 2023Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness
  5. 2022Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections
  6. 2023Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem
  7. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  8. 2024Self-Driving Car Racing: Application of Deep Reinforcement Learning