DI-Drive

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DI-drive is an open-source Decision Intelligence Platform for Autonomous Driving simulation. It allows researchers and engineers to apply different simulators, datasets, and cases for training and testing autonomous driving policies.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is DI-drive?

DI-drive is an open-source Decision Intelligence Platform specifically designed for Autonomous Driving simulation. It provides a unified entry point to apply Decision Intelligence across various driving simulation suits, supporting different simulators, datasets, and cases. The platform is built on DI-engine, a Reinforcement Learning platform, and currently integrates with popular simulators like Carla and MetaDrive. DI-drive enables users to run Imitation Learning, Reinforcement Learning, and GAIL experiments, offering a comprehensive environment for developing and evaluating autonomous driving policies. It includes a Model Zoo with pre-trained models and a Casezoo for scenario-based training and testing, making it a robust tool for advanced research and development in autonomous driving.

Best used for

Ideal for developers and data scientists who need to simulate autonomous driving scenarios, train reinforcement learning policies, and evaluate AI agents for vehicles. Especially valuable for researchers working with Carla or MetaDrive simulators to develop robust autonomous driving systems.

Common actions

simulate autonomous driving

train RL policies

test driving algorithms

develop AI agents

deepfakecollaborationautomated workflowworkflowslow-code/no-codegithub copilotopen-source"AI Agents"face swapping

Capabilities

Key features

Autonomous driving simulation
Supports Carla simulator
Supports MetaDrive simulator
Reinforcement Learning platform
Imitation Learning experiments
Scenario-based training
Model Zoo

Target Audience

developerdata scientist

Integrations

carlametadrive

Pricing & Plans

Open Source

Free

FAQs

What simulators does DI-drive support?

DI-drive currently supports Carla, an open-source Autonomous Driving simulator, and MetaDrive, which provides diverse driving scenarios for Generalizable Reinforcement Learning. Users can install either or both to run simulations within the platform.

What kind of learning methods can be applied using DI-drive?

DI-drive is designed to facilitate various Decision Intelligence methods, including Imitation Learning, Reinforcement Learning, and GAIL. It leverages the DI-engine platform to build most of its running modules and demos for these approaches.

Does DI-drive provide pre-built models or scenarios?

Yes, DI-drive includes a Model Zoo with pre-trained models for different learning methods like Conditional Imitation Learning and BeV Speed RL. It also features a Casezoo, a scenario set for training and testing autonomous driving policies.

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