/toolkit

Official Toolkit for The RoboDrive Challenge

Primary LanguagePython

RoboDrive Toolkit

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Welcome to The RoboDrive Challenge! 👋

🚘 About

RoboDrive is one of the first competitions that targeted probing the Out-of-Distribution (OoD) robustness of state-of-the-art autonomous driving perception models, centered around two mainstream topics: common corruptions and sensor failures.

There are eighteen real-world corruption types in total, ranging from three perspectives:

  • Weather and lighting conditions, such as bright, low-light, foggy, and snowy conditions.
  • Movement and acquisition failures, such as potential blurs caused by vehicle motions.
  • Data processing issues, such as noises and quantizations happen due to hardware malfunctions.

Additionally, we aim to probe the 3D scene perception robustness under camera and LiDAR sensor failures:

  • Loss of certain camera frames during the driving system sensing process.
  • Loss of one or more camera views during the driving system sensing process.
  • Loss of the roof-top LiDAR view during the driving system sensing process.

Kindly visit our webpage to explore more details and instructions of this challenge. 🚙

🌈 Venue

This competition is affiliated with the 41st IEEE Conference on Robotics and Automation (ICRA 2024).


ICRA is the IEEE Robotics and Automation Society's flagship conference. ICRA 2024 will be held from May 13th to 17th, 2024, in Yokohama, Japan.

Outline

ℹ️ Useful Info

# Item Link
🌐 Competition Webpage https://robodrive-24.github.io
🔧 Competition Toolkit https://github.com/robodrive-24/toolkit
:octocat: Official GitHub Account https://github.com/robodrive-24
📫 Contact robodrive.2024@gmail.com

🕐 Timeline

Note: All timestamps are in the AoE (Anywhere on Earth) format.

  • Dec 25 '23 - Team Up; Register for your team by filling in this Google Form.
  • Jan 05 '24 - Release of training and evaluation data.
  • Jan 15 '24 - Competition servers online @ CodaLab.
  • Mar 31 '24 - Phase One deadline.
  • Apr 30 '24 - Phase Two deadline.
  • May 17 '24 - Award decision announcement @ ICRA 2024.

🚙 Challenge Tracks

There are five tracks in this RoboDrive challenge, with emphasis on the following 3D scene perception tasks:

# Task Description Doc Server
Track 1 Robust BEV Detection Evaluating the resilience of detection algorithms against diverse environmental and sensor-based corruptions [Link] [Link]
Track 2 Robust Map Segmentation Focusing on the segmentation of complex driving scene elements in BEV maps under varied driving conditions [Link] [Link]
Track 3 Robust Occupancy Prediction Testing the accuracy of occupancy grid predictions in dynamic and unpredictable real-world driving environments [Link] [Link]
Track 4 Robust Depth Estimation Assessing the depth estimation robustness from multiple perspectives for comprehensive 3D scene perception [Link] [Link]
Track 5 Robust Multi-Modal BEV Detection Tailored for evaluating the reliability of advanced driving perception systems equipped with multiple types of sensors [Link] [Link]

Our evaluation servers were developed based on the CodaLab platform. 🙆‍♂️


CodaLab is an open-source web-based platform that enables researchers, developers, and data scientists to collaborate, with the goal of advancing research fields where machine learning and advanced computation are used.

♨️ Data Preparation

Kindly refer to DATA_PREPARE.md for the details to prepare the training and evaluation data.

🚀 Getting Started

Kindly refer to GET_STARTED.md to learn more usage of this toolkit.

📝 Changelog

  • Jan 22 '24 - The evaluation server of Track 4 is online. [Server4]
  • Jan 15 '24 - The evaluation server of Track 5 is online. [Server5]
  • Jan 15 '24 - The evaluation server of Track 3 is online. [Server3]
  • Jan 15 '24 - The evaluation server of Track 2 is online. [Server2]
  • Jan 15 '24 - The evaluation server of Track 1 is online. [Server1]
  • Jan 12 '24 - Instructions, baseline models, and results of Track 5 have been released.
  • Jan 12 '24 - Training and evaluation data of Track 5 have been released.
  • Jan 08 '24 - A list of Frequently Asked Questions (FAQs) has been summarized for better clarity.
  • Jan 06 '24 - Instructions, baseline models, and results of Tracks 1 to 3 have been released.
  • Jan 05 '24 - Training and evaluation data of Tracks 1 to 4 have been released.
  • Dec 25 '23 - Register for your team by filling in this Google Form.
  • Dec 01 '23 - Launch of The RoboDrive Challenge at ICRA 2024. More info coming soon!

🥇 Awards

The top-performing participants of this competition are honored with cash awards and certificates.

Award Amount Honor
🥇 1st Place To Be Announced Official Certificate
🥈 2nd Place To Be Announced Official Certificate
🥉 3rd Place To Be Announced Official Certificate

Note: The cash awards are donated by our sponsors. We reserve the right to examine the validity of each submission. For more information, kindly refer to the Terms & Conditions section.

License

Creative Commons License
This competition is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

❤️ Sponsor

To be updated.

Citation

If you find this competition helpful for your research, kindly consider citing our papers:

@article{xie2023robobev,
    title = {RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions},
    author = {Shaoyuan Xie and Lingdong Kong and Wenwei Zhang and Jiawei Ren and Liang Pan and Kai Chen and Ziwei Liu},
    journal = {arXiv preprint arXiv:2304.06719}, 
    year = {2023}
}
@inproceedings{kong2023robodepth,
    title = {RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions},
    author = {Lingdong Kong and Shaoyuan Xie and Hanjiang Hu and Lai Xing Ng and Benoit R. Cottereau and Wei Tsang Ooi},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2023},
}
@inproceedings{kong2023robo3d,
    title = {Robo3D: Towards Robust and Reliable 3D Perception against Corruptions},
    author = {Lingdong Kong and Youquan Liu and Xin Li and Runnan Chen and Wenwei Zhang and Jiawei Ren and Liang Pan and Kai Chen and Ziwei Liu},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    pages = {19994-20006},
    year = {2023},
}
@misc{mmdet3d,
    title = {MMDetection3D: OpenMMLab Next-Generation Platform for General 3D Object Detection},
    author = {MMDetection3D Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
    year = {2020}
}

In the meantime, kindly cite the technical reports of the nuScenes dataset and the CodaLab platform:

@inproceedings{caesar2020nuscenes,
    title={nuScenes: A Multimodal Dataset for Autonomous Driving},
    author={Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and Giancarlo Baldan and Oscar Beijbom},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    pages = {11621-11631},
    year={2020}
}
@article{pavao2023codalab,
    title = {CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges},
    author = {Adrien Pavão and Isabelle Guyon and Anne-Catherine Letournel and Dinh-Tuan Tran and Xavier Baró and Hugo Jair Escalante and Sergio Escalera and Tyler Thomas and Zhen Xu},
    journal = {Journal of Machine Learning Research (JMLR)},
    pages = {1-6},
    year = {2023}
}

⚖️ Terms & Conditions

This competition is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

  1. That the data in this competition comes “AS IS”, without express or implied warranty. Although every effort has been made to ensure accuracy, we do not accept any responsibility for errors or omissions.
  2. That you may not use the data in this competition or any derivative work for commercial purposes such as, for example, licensing or selling the data, or using the data with a purpose of procuring a commercial gain.
  3. That you include a reference to RoboDrive (including the benchmark data and the specially generated data for academic challenges) in any work that makes use of the benchmark. For research papers, please cite our preferred publications as listed on our webpage.

To ensure a fair comparison among all participants, we require:

  1. All participants must follow the exact same data configuration when training and evaluating their algorithms. Please do not use any public or private datasets other than those specified for model training.
  2. The theme of this competition is to probe the out-of-distribution robustness of autonomous driving perception models. Therefore, any use of the corruption and sensor failure types designed in this benchmark is strictly prohibited, including any atomic operation that comprises any one of the mentioned corruptions.
  3. To ensure the above two rules are followed, each participant is requested to submit the code with reproducible results before the final result is announced; the code is for examination purposes only and we will manually verify the training and evaluation of each participant's model.

🤔 Frequently Asked Questions
🤔 Q1: "How can I register a valid team for this competition?"
🚙 A1: To register a team, kindly fill in this Google Form. The registration period is from now till the deadline of phase one, i.e., Mar 31 '24.
🤔 Q2: "Are there any restrictions for the registration? For example, the number of team members."
🚙 A2: Each team leader should make a valid registration for his/her team. Each participant can only be registered by one team. There is no restriction on the number of team members in a team.
🤔 Q3: "Whether team members can be changed during the competition?"
🚙 A3: No. You CANNOT change the list of team members after the registration. You must register again as a new team if you need to add or remove any members of your team.
🤔 Q4: "How many tracks can I participate in?"
🚙 A4: Each team can participate in at most two tracks in this competition.
🤔 Q5: "What can I expect from this competition?"
🚙 A5: We provide the winning teams from each track with cash awards 💰 and certificates 🥇. The winning solutions will be summarized as a technical report 📖. An example of last year's technical report can be found here.
🤔 Q6: “Can I use additional data resources for model training?"
🚙 A6: No. All participants must follow the SAME data preparation procedures as listed in DATA_PREPARE.md. Additional data sources are NOT allowed in this competition.
🤔 Q7: "Can I use corruption augmentations during model training?"
🚙 A7: No. The theme of this competition is to probe the out-of-distribution robustness of autonomous driving perception models. Therefore, all participants must REFRAIN from using any corruption simulations as data augmentations during the model training, including any atomic operation that comprises any one of the corruptions in this competition.
🤔 Q8: "How should I configurate the model training? Are there any restrictions on model size, image size, loss function, optimizer, number of epochs, and so on?"
🚙 A8: We provide one baseline model for each track in GET_STARTED.md. The participants are recommended to refer to these baselines as the starting point in configuring the model training. There is no restriction on normal model training configurations, including model size, image size, loss function, optimizer, and number of epochs.
🤔 Q9: "Can I use LiDAR data for Tracks 1 to 4?"
🚙 A9: No. Tracks 1 to 4 are single-modality tracks that only involve the use of camera data. The goal of these tracks is to probe the robustness of perception models under camera-related corruptions. Participants who are interested in multi-modal robustness (camera + LiDAR) can refer to Track 5 in this competition.
🤔 Q10: "Is it permissible to use self-supervised model pre-training (such as MoCo and MAE)?"
🚙 A10: Yes. The use of self-supervised pre-trained models is possible. Such models may include MoCo, MoCo v2, MAE, DINO, and many others. Please make sure to acknowledge (in your code and report) if you use any pre-trained models.
🤔 Q11: "Can I use large models (such as SAM) to generate pre-training or auxiliary annotations?"
🚙 A11: No. The use of large foundation models, such as CLIP, SAM, SEEM, and any other similar models, is NOT allowed in this competition. This is to ensure a relatively fair comparing environment among different teams. Any violations of this rule will be regarded as cheating and the results will be canceled.
🤔 Q12: "Are there any restrictions on the use of pre-trained weights (such as DD3D, ImageNet, COCO, ADE20K, Object365, and so on)?"
🚙 A12: Following the most recent BEV perception works, it is possible to use pre-trained weights on DD3D, ImageNet, and COCO. The use of weights pre-trained on other datasets is NOT allowed in this competition.
🤔 Q13: "Can I combine the training and validation sets for model training?"
🚙 A13: It is strictly NOT allowed to use the validation data for model training. All participants MUST follow the nuScenes official train split during model training and REFRAIN from involving any samples from the validation set. Any violations of this rule will be regarded as cheating and the results will be canceled.
🤔 Q14: "Can I use model ensembling and test-time augmentation (TTA)?"
🚙 A14: Like many other academic competitions, it is possible to use model ensembling and test-time augmentation (TTA) to enhance the model when preparing the submissions. The participants SHOULD include necessary details for the use of model ensembling and TTA in their code and reports.
🤔 Q15: "How many times can I make submissions to the server?"
🚙 A15: For phase one (Jan. - Mar.), a team can submit up to 3 times per day and 99 times total. For phase two (Apr.), a team can submit up to 2 times per day and 49 times total. One team is affiliated with one CodaLab account only. Please REFRAIN from having multiple accounts for the same team.
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Contact

📫 Didn't find a related FAQ to your questions? Let us know (robodrive.2024@gmail.com)!

Organizers

Affiliation

🙆‍♂️ Acknowledgements

This competition is developed based on the RoboBEV, RoboDepth, and Robo3D projects.

This competition toolkit is developed based on the MMDetection3D codebase.


MMDetection3D is an open-source toolbox based on PyTorch, towards the next-generation platform for general 3D perception. It is a part of the OpenMMLab project developed by MMLab.

The evaluation sets of this competition are constructed based on the nuScenes dataset from Motional AD LLC.

Part of the content of this toolkit is adopted from The RoboDepth Challenge @ ICRA 2023.