# MCTS_for_Behavior_Planning

**Repository Path**: wenb11/MCTS_for_Behavior_Planning

## Basic Information

- **Project Name**: MCTS_for_Behavior_Planning
- **Description**: 使用蒙特卡洛树搜索 （MCTS） 的自动驾驶决策
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-14
- **Last Updated**: 2025-01-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Decision-making in Autonomous Driving using Monte Carlo Tree Search (MCTS)

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

This research was presented at the 2024 IEEE International Symposium on Safety Security Rescue Robotics (SSRR), held in New York. 
[View the paper here](https://ieeexplore.ieee.org/document/10770028).

If you find the approaches and data provided in this repository helpful for your research, please consider citing our paper:
```bibtex
@inproceedings{wen2024monte,
  title={Monte Carlo Tree Search for Behavior Planning in Autonomous Driving},
  author={Wen, Qianfeng and Gong, Zhongyi and Zhou, Lifeng and Zhang, Zhongshun},
  booktitle={2024 IEEE International Symposium on Safety Security Rescue Robotics (SSRR)},
  year={2024},
  publisher={IEEE}
}
```

## Abstract

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.

## Setup & Usage

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

## Qualitative Results

## Real-world Road Scenarios

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.

### Roads in Shanghai, China.
<table>
  <tr>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/83dd9359-907e-4bb6-aa78-44418a43dc49" alt="ShanghaiCenturalAvenue" width="60%" height="60%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/ce8fe49c-74ca-403a-9819-9605335e1412" alt="ShanghaiIntersection" width="60%" height="60%"/></p></td>  
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/a9f30729-97de-4581-bbac-6b33526b4a9e" alt="ShanghaiLujiazuiTunnel" width="60%" height="60%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/fe49adf7-2321-4a05-84d2-365673c63e91" alt="ShanghaiRoundAbout" width="60%" height="60%"/></p></td>
  </tr>
</table>


   
### Birds Eye View:
<table>
  <tr>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/f8db1436-c636-4cb4-bea6-644598532c51" alt="ShanghaiCenturalAvenue" width="70%" height="70%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/0a05ae5d-89cf-4229-a139-37e5ed4f3e4e" alt="ShanghaiLujiazuiTunnel" width="70%" height="70%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/d26e3e95-eae2-49cb-8cfb-931f554dfd2e" alt="ShanghaiLujiazuiTunnel" width="70%" height="70%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/b9c8fb1c-fa36-406a-800e-9e421691273e" alt="ShanghaiRoundAbout" width="70%" height="70%"/></p></td>
  </tr>
</table>
   
### Typical Results:
<table>
  <tr>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/226da03d-0813-4fd8-8183-2fed35b58bc8" alt="ShanghaiCenturalAvenue" width="80%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/577aab35-06c5-45c6-89ea-d8f2db4d6dca" alt="ShanghaiLujiazuiTunnel" width="80%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/5f16817a-3696-49a7-8176-8b04063c007f" alt="ShanghaiLujiazuiTunnel" width="80%"/></p></td>
    <td><p align="center"><img src="https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/a409bf8f-17e2-40ca-87ec-2b6e8c8b0f8a" alt="ShanghaiRoundAbout" width="80%"/></p></td>
  </tr>
</table>


##  Typical Highway Scenarios
 
### Qualitative Results 1
![ds4_lanes_highWay_advanced](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/cb822561-bc23-4ace-8c89-3525fa4b9c68)
_Figure 1: Highway Exit (HE) example._

 
### Qualitative Results 2
![large_curvature](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/b05f2430-7404-40a0-ae2e-2f172bc2ddd5)
_Figure 2: Large Curvature example._

##  Typical Urban Scenarios

 
### Qualitative Results 3
![ds6_lanes_roadWith5Cars_2Cars_CuttingIn](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/ed08e5d3-db53-410c-9f08-d6a2746dac0f)
#### _Figure 3: Intersection, Straight example._
 
### Qualitative Results 4
![ds6_lanes_roadWith5Cars_egoTurningLeft](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/18a29055-820c-48cc-8276-66854dd5b6a3)
#### _Figure 4: Intersection, Unprotected Left Turn example._

 
### Qualitative Results 5
![ds6_lanes_roadWith5Cars_egoTurningLeft_2](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/6061074a-23c6-4cf0-9f1c-1d8580980a2d)
#### _Figure 5: Intersection, Unprotected Left Turn example 2._

 
### Qualitative Results 6
![ds6_lanes_roadWith5Cars_egoTurningRight_2](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/999695b9-90c9-4db8-92bb-0239679cbd35)
#### _Figure 6: Intersection, Unprotected Right Turn example._
 
### Qualitative Results 7
![ds6_lanes_roadWith5Cars_horizontal_crossing](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/23ce25f6-fecd-4872-91a0-a05a0851cb28)
#### _Figure 7: Intersection, Unprotected Straight Cross example._
 
### Qualitative Results 8
![ds6_lanes_roadWith5Cars_stopping](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/2905e5ab-d3da-4bff-afed-3ca04cfdf802)
#### _Figure 8: Intersection, Blocked example._
 
### Qualitative Results 9
![ds6_lanes_roadWith5Cars_stucked](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/38c22f47-42e7-4cc5-9a52-9dad6c68906c)
#### _Figure 9: Intersection, Blocked by Stationary Objects example._
 
### Qualitative Results 10
![ds6_lanes_roadWith5CarsCuttingIn](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/dc96ed33-df67-476e-9ac4-8d6b1c2caabc)
#### _Figure 10: Intersection, Go Straight example._

 
### Qualitative Results 11
![ds6_lanes_roadWith5CarsTurningLeft](https://github.com/zhongshun/MCTS_for_Behavior_Planning/assets/14044932/528976bf-5494-4f5f-929a-8a0214f10c01)
#### _Figure 11: Intersection, Go Straight example 2._