mlsanityData to 7 Oct 2026

Cooperative Driving at Unsignalized Intersections Using Tree Search

arXiv 1902.01024 ↗First posted 4 Feb 2019cs.MAcited by 10

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Abstract (arXiv)

In this paper, we propose a new cooperative driving strategy for connected and automated vehicles (CAVs) at unsignalized intersections. Based on the tree representation of the solution space for the passing order, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal passing order (leaf node) within a very short planning time. Testing results show that this new strategy can keep a good tradeoff between performance and computation flexibility.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 4; 2025: 0; 2026: 2.

10 papers.

  1. 2024Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Frameworkcited by 16
  2. 2021Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlookscited by 15
  3. 2023Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approachcited by 8
  4. 2024Multi-agent Path Finding for Cooperative Autonomous Drivingcited by 3
  5. 2022Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersectionscited by 2
  6. 2024A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehiclescited by 2
  7. 2022Coordinating CAV Swarms at Intersections with a Deep Learning Modelcited by 1
  8. 2024Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersectionscited by 0
  9. 2026Large Language Model based Interactive Decision-Making for Autonomous Drivingcited by 0
  10. 2026CoLMIN: LLM-based Multi-Decision Path Negotiation for Cooperative Autonomous Drivingcited by 0