ApolloAuto/apollo

An open autonomous driving platform

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ApolloAuto/apollo has 26k+ stars and 9.5k+ forks on GitHub, is written in C++, and was last pushed on Apr 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 1 hour ago
Added to GitGenius on May 27th, 2026
Created on July 4th, 2017
Open Issues & Pull Requests: 1,050 (+0)
GitHub issues: Enabled
Number of forks: 9,954
Total Stargazers: 26,867 (+0)
Total Subscribers: 1,098 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 43.4 hours
Mean response time: 34.0 days
90th percentile: 52.4 days
Tracked items: 341

How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "Module: Others" is answered fastest, typically in about 7 hours, while "Module: Perception" waits about 2 days. 80% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 29% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 232
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 592 days
Stale 30+ days: 230
Stale 90+ days: 228

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 1
Events in 7 days: 1

Top labels

  • Module: Perception (27)
  • Module: Simulation & Dreamview (19)
  • Module:Cyber (18)
  • Module: Others (16)
  • Module: Planning (16)
  • Module: Build (12)
  • Type: Help wanted (11)
  • Module: Driver (8)

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Detailed Description

Apollo is an open-source autonomous driving platform developed by Baidu that provides a comprehensive software framework for building, testing, and deploying self-driving vehicles. Written primarily in C++, the platform addresses the full spectrum of autonomous vehicle development including perception, sensor fusion, localization, path planning, control algorithms, mapping, and simulation capabilities.

The repository's contributor base overlaps significantly with major machine learning and computer vision projects including OpenCV, PyTorch, and TensorFlow, reflecting Apollo's reliance on these foundational technologies.

Apollo's architecture has evolved through nine major versions, each expanding autonomous driving capabilities. Version 1.0 introduced automatic GPS waypoint following for enclosed venues like test tracks. Version 1.5 added LiDAR-based perception for fixed lane cruising. Version 2.0 enabled autonomous driving on simple urban roads with collision avoidance and traffic light recognition. Version 2.5 introduced highway autonomous driving with camera-based obstacle detection. Version 3.0 and 3.5 progressively added support for complex urban scenarios including residential areas and unprotected turns. Version 5.0 focused on production-ready geo-fenced autonomous driving, while version 5.5 introduced curb-to-curb urban driving capabilities. Version 6.0 enhanced deep learning models and introduced data pipeline services. Version 7.0 added Apollo Studio as an online development platform alongside new perception and prediction models. Version 8.0 introduced a modular "Package" system for code organization and integrated model training, deployment, and validation tools. Version 9.0 continues this evolution with additional capabilities.

The platform requires specific hardware and software prerequisites. Vehicles must have complete by-wire systems including brake-by-wire, steering-by-wire, throttle-by-wire, and shift-by-wire capabilities, with testing performed on Lincoln MKZ vehicles. Development machines need at least an 8-core processor and 16GB memory, with NVIDIA Turing or AMD GFX9/RDNA/CDNA GPUs strongly recommended. As of November 2024, Apollo upgraded to CUDA 11.8 to support NVIDIA Ada Lovelace GPUs with driver version 520.61.05 or higher. The platform supports Ubuntu 18.04, 20.04, and 22.04, requires Docker-CE 19.03 and above, and the NVIDIA Container Toolkit for GPU support. The documentation recommends installing Apollo versions sequentially starting from 1.0 to ensure proper hardware validation before advancing to more capable versions.