# metadriverse/metadrive

MetaDrive: Lightweight driving simulator for everyone

Repository: https://github.com/metadriverse/metadrive
Canonical: https://ross.abutalabs.com/products/metadrive
Homepage: https://metadriverse.github.io/metadrive/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-08-15T18:57:56+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 37, release rhythm 8, longevity 100
- inputs: {"age_days": 1831, "days_push": 383, "days_rel": 635, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1235, forks 197 (observed 2026-08-28T04:04:04.940146+00:00)

## What it is
MetaDrive is an open-source, lightweight driving simulator built for AI and autonomy research, supporting compositional scene synthesis and reconstruction of real-world driving logs. It provides realistic physics and multiple sensor modalities (RGB, depth, semantic, LiDAR point cloud) and runs at over 1000 FPS on standard PCs.

## Use cases
- train reinforcement learning agents for autonomous driving
- benchmark generalizable RL across unseen driving scenes
- simulate safe exploration and safety-critical driving scenarios
- reconstruct driving environments from Waymo or nuPlan datasets
- generate synthetic driving scenes with custom road maps and traffic
- collect camera and LiDAR sensor data for perception research
- run multi-agent driving behavior simulations

## When to choose
- you need a fast, easy-to-install driving simulator for RL research
- you want to train or evaluate driving policies in Python with Gym-style APIs
- you need sensor simulation (RGB, depth, LiDAR) without heavy commercial simulators
- you want to replay real-world datasets like Waymo Open Dataset in simulation
- you study safe RL or multi-agent driving behaviors

## When to avoid
- you need photorealistic rendering comparable to CARLA or commercial simulators
- you require full vehicle dynamics validation for hardware deployment
- you need non-driving robotics simulation
- you want a plug-and-play product rather than a research codebase

## Facets
- artifact type: library
- maturity: active
- function: simulation, machine-learning, reinforcement-learning, computer-vision, game-engine
- domain: autonomous-vehicles, simulation, reinforcement-learning, machine-learning, robotics
- platform: windows, python, cross-platform
- tags: driving-simulator, autonomous-driving, reinforcement-learning-environment, gym-environment, scene-generation, sensor-simulation, safe-rl, multi-agent, linux, macos

## Member repositories
- metadriverse/metadrive (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.940146+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:06.942418+00:00, confidence not recorded.
  - readme: https://github.com/metadriverse/metadrive (fetched 2026-08-28T04:04:04.940146+00:00, sha 17a06cb98d7b)
  - homepage: https://metadriverse.github.io/metadrive/ (fetched 2026-08-29T12:21:48.982165+00:00, sha 6fa76f675a1b)
- Data as of 2026-08-30T08:39:29.467469+00:00.