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navsim's Introduction

Autonomous Vision Blog

This is the blog of the Autonomous Vision Group at MPI-IS Tübingen and University of Tübingen. You can visit our blog at https://autonomousvision.github.io. Also check out our website to learn more about our research.

Overview

Creating a blog post follows the usual git workflow:

  1. clone repository:

    git clone https://github.com/autonomousvision/autonomousvision.github.io.git
    
  2. create new branch for your post:

    git branch my-post
    git checkout my-post
    
  3. work on branch / push my-post branch for collaboration

  4. rebase master on your branch and squash commits (note that all your commits to master will be visible in the git history):

    git checkout master
    git rebase -i my-post
    
  5. push master

    git push origin master
    
  6. delete your branch

    locally:

    git branch -d my-post
    

    and remotely if you pushed your branch in step 3:

    git push origin --delete my-post
    

Instructions for Authors

To write a new blog entry, first register yourself as an author in authors.yml. Here, you can also add your email address and links to your social media accounts etc.

You can then create a new blog post by adding a markdown or html file in the _posts folder. Please use the format YYYY-MM-DD-YOUR_TITLE.{md,html} for naming the file. You can then create a yaml header where you specify the author, the category of the post, tags, etc. For more information, take a look at existing posts and the Minimal Mistakes documentation.

If you want to include images or other assets, create a subfolder in the assets/posts folder with the same name as the filename of your blog post (without extension). You can simply reference your assets in your post using {{ site.url }}/assets/posts/YYYY-MM-DD-YOUR_TITLE/ followed by the filename of the corresponding asset. Make sure that you don't forget to include the {{ site.url }}! While the post while be rendered correctly without the {{ site.url }}, the images in the newsfeed will break if you don't include it.

Please keep in mind that all your commits to master will appear in the git history. To keep this history clean, it might make sense to edit your post in a separate (private) branch and then merge this branch into master.

Offline editing

When you do offline editing, you probably want to build the website offline for a preview. To this end, you first have to install Ruby and Jekyll. Then, you have to install the dependencies (called Gems) for the website:

bundle

Now, you are ready to build and serve the website using

 bundle exec jekyll serve

Sometimes Jekyll hiccups over character encoding. In this case, try

 LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8 bundle exec jekyll serve

If you encounter GemNotFoundException, try to remove

BUNDLED WITH
    2.0.1

from Gemfile.lock.

This command will build the website and serve it at http://localhost:4000. When you save changes, the website will be automatically rebuilt in the background. Note, however, that changes to _config.yaml will not be tracked which means that you have to restart the jekyll server after configuration changes.

References

You can find more information here:

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navsim's Issues

Questions about Navtrain Dataset and warmup_test

Hi everyone, I notice that there was an update in the navsim repository including dataset, new baseline, visualizations, thanks for the great work. I have a question about the navtrain dataset, when I downloaded it, it seemed there is no current_split_5 and history_split_5, which included in the download script(as shown below). Did I miss something or there is no such data? And there is no corrresponding pkl file, which means we can't use it for our training, will it be provided later?

image

Another Question is that privously I split the mini set into train, val, test for small testing, and upload the trained model on the warmup track. But I notice that you said that the warmup_test_e2e is a scene filter for the mini split. So can I ask which part of the mini set you use for the warmup track?

Thanks a lot for answering my question!

Log Pickle Loading Fails

When I run ./run_cv_pdm_score_evaluation.sh, there is a bug when loading log file. I use the pickle files you provide.
image

Question about the private test set

Hi, I've downloaded the private test set downloaded from https://huggingface.co/datasets/OpenDriveLab/OpenScene/resolve/main/openscene-v1.1/openscene_metadata_private_test_e2e.tgz and https://huggingface.co/datasets/OpenDriveLab/OpenScene/resolve/main/openscene-v1.1/openscene_sensor_private_test_e2e.tgz according to your script. It seems that this private set is mainly based on the previous test set from https://huggingface.co/datasets/OpenDriveLab/OpenScene/resolve/main/openscene-v1.1/openscene_metadata_test.tgz?

The previous test set contains annotations like bboxes and HD map according to OpenScene. I feel like asking how can we maintain the integrity of the challenge if these data are visible to participants?

About required environment variables

Hi! Thank you for this incredible work! I'd like to clarify the proper method of setting up the necessary environment variables according to your README documentation located here: https://github.com/autonomousvision/navsim/blob/main/docs/install.md#2-install-the-navsim-devkit.

After following your guide to prepare the dataset and code, I have reached the stage where I need to define the paths for the environment variables. However, I am uncertain about the specific directories to use for each of the following:

export NAVSIM_DEVKIT_ROOT=/path/to/navsim/devkit  # Should this be the path to the root directory of my navsim installation?
export NUPLAN_EXP_ROOT=/path/to/navsim/exp # Is this the path pointing to the 'navsim_logs' folder that I've downloaded?
export NUPLAN_MAPS_ROOT=/path/to/nuplan/maps # The path to the downloaded 'nuplan-maps-v1.0' folder?
export OPENSCENE_DATA_ROOT=/path/to/openscene # Do I need to acquire a separate dataset specifically for this variable?

Your guidance in specifying these paths would be greatly appreciated!

Aligning lidar points‘ coordinates across different frames?

Currently, I am using ego2global transformation matrix to warp LiDAR points’ in the history to the current key frame. If this matrix isn’t available in the final test set, it might be difficult to do so. Can you provide the transformation matrix between different frames for the test set?

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