This repository contains the code, datasets, and supplementary materials related to our research paper on leveraging MCTS for decision-making in autonomous vehicles.

We present a comprehensive framework based on Monte Carlo Tree Search for decision-making in autonomous driving scenarios. Through extensive simulations in MATLAB's autonomous driving toolbox 2023a (Note that some functions may not supported for lower version.). We showcase the framework's efficacy across various driving conditions, from intricate urban intersections to highway exits. While our simulations demonstrate promising results, we highlight areas for potential improvement and suggest future research directions.

1. Clone the repository
2. Choose or set the environment. Make sure the version of MATLAB is above 2023a and the version of Automated Driving Toolbox is above 3.7.
3. Run mctsPlanning.m

We demonstrate the effectiveness of our autonomous driving algorithm in navigating through typical real-world environments, from left to right: Navigation through an Intersection, Unprotected Left Turn at Intersection, Managing Merging and Diverging, and Navigation through a Roundabout.

Figure 1: Highway Exit (HE) example.

Figure 2: Large Curvature example.