Home Projects AlphaDrive
AlphaDrive
Python

AlphaDrive

Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning

by hustvl · GitHub
Stars
Forks
License
Created
Last commit
Language
autonomous-drivinggrpoplanningApache-2.0Python
View on GitHub
In plain words

Develop autonomous driving systems by combining AI reasoning and learning techniques.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
AlphaDrive — GitHub preview card
📈 Star history
333332
2026-07-202026-08-31
📈 Track AlphaDrive

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning

AlphaDrive has 332 stars on GitHub. It has been forked 18 times. AlphaDrive is written mainly in Python. It has been in active development since 2025. AlphaDrive is available under the Apache-2.0 license. Its main topics are autonomous-driving, grpo, planning, reasoning.

Frequently asked questions

What is AlphaDrive?

Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning

Is AlphaDrive open source?

AlphaDrive is an open-source project. It is released under the Apache-2.0 license.

Is AlphaDrive free?

Yes. AlphaDrive is free and open source — you can use, modify and self-host it.

What license does AlphaDrive use?

AlphaDrive is available under the Apache-2.0 license.

What language is AlphaDrive written in?

AlphaDrive is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — AlphaDrive

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=hustvl-alphadrive)](https://olud.ai/project/hustvl-alphadrive.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.