ldkong1205 / toolkit

Official Toolkit for The RoboDrive Challenge

RoboDrive Toolkit

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 the RoboDrive 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.

The ICRA competitions provide a unique venue for state-of-the-art technical demonstrations from research labs throughout academia and industry. For additional details, kindly refer to the ICRA 2024 website.

Outline

• Changelog
• Useful Info
• Timeline
• Challenge Tracks
• Awards
• Data Preparation
• Getting Started
• Citation
• License
• Sponsor
• Terms & Conditions
• Frequently Asked Questions
• Acknowledgements

📝 Changelog

• Jan 08 '24 - A list of Frequently Asked Questions has been summarized on this page.
• 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!

ℹ️ Useful Info

#	Item	Link
🌐	Competition Webpage	https://robodrive-24.github.io
🔧	Competition Toolkit	https://github.com/robodrive-24/toolkit
	Official GitHub Account	https://github.com/robodrive-24
📫	Contact	[e-mail]

🕐 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 @ EvalAI.
• 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]

🥇 Awards

To be announced.

♨️ 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.

Citation

If you find this competition helpful for your research, please 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},
}

License

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

❤️ Sponsor

To be updated.

⚖️ 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. This 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: To be updated.
🤔	Q13: "Can I combine the training and validation sets for model training?"
🚙	A13: [Updated]. 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.
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Contact

📫 Didn't find a related FAQ to your questions? Let us know ([e-mail])!

🙆‍♂️ 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.