# Cooperative Driving at Unsignalized Intersections Using Tree Search

arXiv: https://arxiv.org/abs/1902.01024 · HTML: https://mlsanity.com/paper/1902.01024/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework](https://mlsanity.com/paper/2409.12812/) (2024)
- [Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks](https://mlsanity.com/paper/2106.13052/) (2021)
- [Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach](https://mlsanity.com/paper/2305.03563/) (2023)
- [Multi-agent Path Finding for Cooperative Autonomous Driving](https://mlsanity.com/paper/2402.00334/) (2024)
- [Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections](https://mlsanity.com/paper/2205.01278/) (2022)
- [A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles](https://mlsanity.com/paper/2409.13783/) (2024)
- [Coordinating CAV Swarms at Intersections with a Deep Learning Model](https://mlsanity.com/paper/2211.05297/) (2022)
- [Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections](https://mlsanity.com/paper/2409.17162/) (2024)
- [Large Language Model based Interactive Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2604.23513/) (2026)
- [CoLMIN: LLM-based Multi-Decision Path Negotiation for Cooperative Autonomous Driving](https://mlsanity.com/paper/2609.04807/) (2026)

## 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.