ERDOS is a platform for developing self-driving cars and robotics applications.
ERDOS is known to work on Ubuntu 18.04 and 20.04.
To develop an ERDOS application in Rust, simply include ERDOS in Cargo.toml.
The latest ERDOS release is published on
Crates.io
and documentation is available on Docs.rs.
If you'd like to contribute to ERDOS, first install Rust. Then run the following to clone the repository and build ERDOS:
git clone https://github.com/erdos-project/erdos.git && cd erdos
cargo buildTo develop an ERDOS application in Python, simply run
pip install erdos. Documentation is available on
Read the Docs.
If you'd like to contribute to ERDOS, first install Rust. Within a virtual environment, run the following to clone the repository and build ERDOS:
git clone https://github.com/erdos-project/erdos.git && cd erdos/python
pip3 install maturin
maturin developThe Python-Rust bridge interface is developed in the python crate, which
also contains user-facing python files under the python/erdos directory.
If you'd like to build ERDOS for release (better performance, but longer
build times), run maturin develop --release.
python3 python/examples/simple_pipeline.pyERDOS provides Python and Rust interfaces for developing applications.
The Python interface provides easy integration with popular libraries such as tensorflow, but comes at the cost of performance (e.g. slower serialization and the lack of parallelism within a process).
The Rust interface provides more safety guarantees (e.g. compile-time type checking) and faster performance (e.g. multithreading and zero-copy message passing). High performance, safety critical applications such as self-driving car pipelines deployed in production should use the Rust API to take full advantage of ERDOS.
ERDOS is a streaming dataflow system designed for self-driving car pipelines and robotics applications.
Components of the pipelines are implemented as operators which are connected by data streams. The set of operators and streams forms the dataflow graph, the representation of the pipline that ERDOS processes.
Applications define the dataflow graph by connecting operators to streams in the driver section of the program. Operators are typically implemented elsewhere.
ERDOS is designed for low latency. Self-driving car pipelines require end-to-end deadlines on the order of hundreds of milliseconds for safe driving. Similarly, self-driving cars typically process gigabytes per second of data on small clusters. Therefore, ERDOS is optimized to send small amounts of data (gigabytes as opposed to terabytes) as quickly as possible.
ERDOS provides determinism through watermarks. Low watermarks are a bound on the age of messages received and operators will ignore any messages older than the most recent watermark received. By processing on watermarks, applications can avoid non-determinism from processing messages out of order.
To read more about the ideas behind ERDOS, refer to our paper, D3: A Dynamic Deadline-Driven Approach for Building Autonomous Vehicles. If you find ERDOS useful to your work, please consider citing our paper:
@inproceedings{gog2022d3,
title={D3: a dynamic deadline-driven approach for building autonomous vehicles},
author={Gog, Ionel and Kalra, Sukrit and Schafhalter, Peter and Gonzalez, Joseph E and Stoica, Ion},
booktitle={Proceedings of the Seventeenth European Conference on Computer Systems},
pages={453--471},
year={2022}
}We are actively developing an AV platform atop ERDOS! For more information, see the Pylot repository.
If you would like to contact us, you can:
- Community on Slack: Join our community on Slack for discussions about development, questions about usage, and feature requests.
- Github Issues: For reporting bugs.
We always welcome contributions to ERDOS. One way to get started is to pick one of the issues tagged with good first issue -- these are usually good issues that help you familiarize yourself with the ERDOS code base. Please submit contributions using pull requests.