AutonomousDrivingCookbook

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AutonomousDrivingCookbook is an Open Source tool that provides scenarios, tutorials, and demos for autonomous driving. It helps users quickly get acquainted with different onboarding scenarios in autonomous driving.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is AutonomousDrivingCookbook?

The AutonomousDrivingCookbook, developed by Microsoft Garage, offers a collection of scenarios, tutorials, and demos for autonomous driving. It aims to lower the barrier to entry for individuals interested in this rapidly evolving field. The cookbook provides resources for beginners, researchers, and industry experts, with tutorials presented as Jupyter notebooks for easy access and minimal setup. These tutorials leverage popular open-source tools like Keras and TensorFlow, alongside Microsoft technologies such as AirSim and Azure, focusing on content and learning rather than specific tool dependencies. It's a work in progress, with ongoing additions based on user feedback and collaborator availability.

Best used for

Ideal for students and researchers who need to understand autonomous driving concepts, implement deep learning models, and simulate driving scenarios. Especially valuable for those looking for practical, hands-on tutorials using open-source tools and Microsoft technologies.

Common actions

learn autonomous driving

explore deep learning

simulate driving scenarios

develop AI models

workflowsautomated workflowlow-code/no-codecollaborationdeepfake"AI Agents"open-sourcegithub copilotface swapping

Capabilities

Key features

Jupyter notebook tutorials
Autonomous driving scenarios
Deep learning examples
Reinforcement learning examples
AirSim integration
Datasets and scripts

Target Audience

studentteacher

Integrations

tensorflowkerasairsim

Pricing & Plans

Open Source

Free

FAQs

What kind of tutorials are available in the Autonomous Driving Cookbook?

The cookbook currently offers tutorials on autonomous driving using end-to-end deep learning with AirSim and distributed deep reinforcement learning. Future tutorials will include lane detection using deep learning, with more content planned based on user requests.

What tools and technologies are used in the tutorials?

The tutorials leverage popular open-source tools like Keras and TensorFlow. They also integrate Microsoft's open-source and commercial technologies, including AirSim, Azure virtual machines, Batch AI, and CNTK, to provide comprehensive learning experiences.

How can I contribute to the Autonomous Driving Cookbook?

Contributions and suggestions are welcome. You can read the instructions and guidelines for collaborators on the GitHub page. Most contributions require agreeing to a Contributor License Agreement (CLA) from Microsoft.

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