All Projects → Autoware-AI → Autoware.ai

Autoware-AI / Autoware.ai

Licence: apache-2.0
Open-source software for self-driving vehicles

Projects that are alternatives of or similar to Autoware.ai

ACSC
Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems
Stars: ✭ 210 (-95.84%)
Mutual labels:  calibration, autonomous-vehicles
mader
Trajectory Planner in Multi-Agent and Dynamic Environments
Stars: ✭ 252 (-95%)
Mutual labels:  planner, ros
LiDARTag
This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
Stars: ✭ 161 (-96.81%)
Mutual labels:  detection, calibration
Apm planner
APM Planner Ground Control Station (Qt)
Stars: ✭ 413 (-91.81%)
Mutual labels:  ros, autonomous-vehicles
erdos
Dataflow system for building self-driving car and robotics applications.
Stars: ✭ 135 (-97.32%)
Mutual labels:  ros, autonomous-vehicles
Bonnetal
Bonnet and then some! Deep Learning Framework for various Image Recognition Tasks. Photogrammetry and Robotics Lab, University of Bonn
Stars: ✭ 202 (-96%)
Mutual labels:  ros, detection
yac
YAC - Yet Another Calibrator
Stars: ✭ 19 (-99.62%)
Mutual labels:  ros, calibration
Grl
Robotics tools in C++11. Implements soft real time arm drivers for Kuka LBR iiwa plus V-REP, ROS, Constrained Optimization based planning, Hand Eye Calibration and Inverse Kinematics integration.
Stars: ✭ 105 (-97.92%)
Mutual labels:  ros, calibration
FusionAD
An open source autonomous driving stack by San Jose State University Autonomous Driving Team
Stars: ✭ 30 (-99.41%)
Mutual labels:  ros, autonomous-vehicles
ar-tu-do
ROS & Gazebo project for 1/10th scale self-driving race cars
Stars: ✭ 65 (-98.71%)
Mutual labels:  ros, autonomous-vehicles
Jetson Car
Autonomous Racing Car using NVIDIA Jetson TX2 using end-to-end driving approach. Paper: https://arxiv.org/abs/1604.07316
Stars: ✭ 172 (-96.59%)
Mutual labels:  ros, autonomous-vehicles
Easy handeye
Automated, hardware-independent Hand-Eye Calibration
Stars: ✭ 355 (-92.96%)
Mutual labels:  ros, calibration
Robot calibration
Generic calibration for robots
Stars: ✭ 154 (-96.95%)
Mutual labels:  ros, calibration
Carma Platform
CARMA Platform is built on robot operating system (ROS) and utilizes open source software (OSS) that enables Cooperative Driving Automation (CDA) features to allow Automated Driving Systems to interact and cooperate with infrastructure and other vehicles through communication.
Stars: ✭ 243 (-95.18%)
Mutual labels:  ros, autonomous-vehicles
Self Driving Golf Cart
Be Driven 🚘
Stars: ✭ 147 (-97.09%)
Mutual labels:  ros, autonomous-vehicles
Autoware Toolbox
MATLAB/Simulink sample code suite for Autoware.
Stars: ✭ 53 (-98.95%)
Mutual labels:  ros, autoware
Webots
Webots Robot Simulator
Stars: ✭ 1,324 (-73.75%)
Mutual labels:  ros, autonomous-vehicles
Robot cal tools
A suite of tools focused on calibration of sensors for robotic workcell development
Stars: ✭ 96 (-98.1%)
Mutual labels:  ros, calibration
installRACECARJ
Install the ROS stack, MIT RACECAR Packages, and hardware support on RACECAR/J.
Stars: ✭ 28 (-99.44%)
Mutual labels:  ros, autonomous-vehicles
Segmenters lib
The LiDAR segmenters library, for segmentation-based detection.
Stars: ✭ 269 (-94.67%)
Mutual labels:  ros, detection

Autoware

Native CI workflow CUDA CI workflow Cross CI workflow

Autoware is the world's first "all-in-one" open-source software for self-driving vehicles. The capabilities of Autoware are primarily well-suited for urban cities, but highways, freeways, mesomountaineous regions, and geofenced areas can be also covered. The code base of Autoware is protected by the Apache 2 License. Please use it at your own discretion. For safe use, we provide a ROSBAG-based simulation environment for those who do not own real autonomous vehicles. If you plan to use Autoware with real autonomous vehicles, please formulate safety measures and assessment of risk before field testing.

You may refer to Autoware Wiki for Users Guide and Developers Guide.

What Is Autoware

Autoware Overview

Autoware provides a rich set of self-driving modules composed of sensing, computing, and actuation capabilities. An overview of those capabilities is described here. Keywords include Localization, Mapping, Object Detection & Tracking, Traffic Light Recognition, Mission & Motion Planning, Trajectory Generation, Lane Detection & Selection, Vehicle Control, Sensor Fusion, Cameras, LiDARs, RADARs, Deep Learning, Rule-based System, Connected Navigation, Logging, Virtual Reality, and so on.

Free manuals can be also found at Autoware-Manuals. You are encouraged to contribute to the maintenance of these manuals. Thank you for your cooperation!

Getting Started

Autoware Demo

Recommended System Specifications

  • Number of CPU cores: 8
  • RAM size: 32GB
  • Storage size: 64GB+

Users Guide

  1. Installation
    1. Docker
    2. Source
  2. Demo
  3. Field Test
  4. Simulation Test
  5. Videos

Developers Guide

  1. Contribution Rules (Must Read)
  2. Overview
  3. Specification

Research Papers for Citation

  1. S. Kato, S. Tokunaga, Y. Maruyama, S. Maeda, M. Hirabayashi, Y. Kitsukawa, A. Monrroy, T. Ando, Y. Fujii, and T. Azumi,``Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems,'' In Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS2018), pp. 287-296, 2018. Link

  2. S. Kato, E. Takeuchi, Y. Ishiguro, Y. Ninomiya, K. Takeda, and T. Hamada. ``An Open Approach to Autonomous Vehicles,'' IEEE Micro, Vol. 35, No. 6, pp. 60-69, 2015. Link

Cloud Services

Autoware Online

You may test Autoware at Autoware Online. No need to install the Autoware repository to your local environment.

Automan

You may annotate and train your ROSBAG data using your web browser through Automan. The trained models can be used for deep neural network algorithms in Autoware, such as SSD and Yolo.

ROSBAG STORE

You may download a number of test and simulation data sets from Tier IV's ROSBAG STORE. Note that free accounts would not allow you to access image data due to privacy matters.

Map Tools

You may create 3D map data through Tier IV's Map Tools. The 3D map data used in Autoware are composed of point cloud structure data and vector feature data.

License

Autoware is provided under the Apache 2 License.

Contact

Autoware Discourse

Autoware Developers Slack Team

Please see the Support Guidelines for more details about getting help.


Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].