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README.md

README.md

RareSim

Coming soon!

This is the reference implementation for our paper:

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation PDF

Matthew O'Kelly*, Aman Sinha*, Hongseok Namkoong*, John Duchi, Russ Tedrake

Abstract: While recent developments in autonomous vehicle technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the de facto evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims. We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning based perception and control algorithms. Using adaptive importance-sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution (learned from real-world data) governing standard traffic behavior. We demonstrate our framework on a highway scenario, our evaluation is 2-20 faster than naive Monte Carlo sampling methods and 10-300 times (where P is the number of processors) faster than real-world testing.

Citing

If you find this code useful in your work, please consider citing:

@inproceedings{okelly2018,
  title={Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation},
  author={O'Kelly*, Matthew and Sinha*, Aman and Namkoong*, Hongseok and Tedrake, Russ and Duchi, John},
  booktitle={Advances in Neural Information Processing Systems},
  year={2018}
}

Dependencies

Requires:

  • docker
  • nvidia-docker2
  • A recent Nvidia GPU e.g. GTX980 or better.

Installation

The docker image essentially packages all dependencies in a safe environment. The scripts we provide will externally mount our source code, and our models, into the docker environment.

Most source code for this project is in Python and so once the docker image is built we won't need any compiling.

Docker and Nvidia-Docker

The following is all of the steps to build a docker image for RareSim from a fresh Ubuntu installation:

  1. Install Docker for Ubuntu. Make sure to sudo usermod -aG docker your-user and then not run below docker scripts as sudo
  2. Install nvidia-docker. Make sure to use nvidia-docker2 not nvidia-docker1.
sudo apt-get install -y nvidia-docker2

You can test that your nvidia-docker installation is working by running

nvidia-docker run --rm nvidia/cuda nvidia-smi

If you get errors about nvidia-modprobe not being installed, install it by running

sudo apt-get install nvidia-modprobe

and then restart your machine.

Getting the RareSim Docker Image

Coming soon

License

Copyright © Aman Sinha, Matthew O'Kelly, Hongseok Namkoong 2018. All rights reserved.

This code is provided under the Creative Commons CC BY-NC-SA 4.0 license which allows for non-commercial use only. For any other use of the software not covered by the terms of this licence, please contact the authors.