I'm trying to download the tbv dataset and it seems there are two instructions to do so. Do these two methods produce the same result?

One here:

1. https://github.com/argoai/argoverse2-api/blob/main/DOWNLOAD.md s5cmd --no-sign-request cp s3://argoai-argoverse/av2/tbv/* target-directory

And another here: 2. https://github.com/argoai/argoverse2-api/blob/main/src/av2/datasets/tbv/README.md SHARD_DIR={DESIRED PATH FOR TAR.GZ files} s5cmd cp s3://argoai-argoverse/av2/tars/tbv/*.tar.gz ${SHARD_DIR}

When I try 1, I get an error "s5cmd is hitting the max open file limit allowed by your OS. Either increase the open file limit or try to decrease the number of workers with '-numworkers' parameter'.

When I try 2, I get an error "Error session: fetching region failed: NoCredentialProviders: no valid providers in chain. Deprecated."

1. probably downloads half of the dataset, while 2. doesn't initiate the download. I will probably continue with 1, but 2. probably is faster. I'm using Linux Ubuntu 18.04.

The Submission Guidelines have nothing about the submission format, could you give more details? Or could you provide a submission sample? Thank you very much!

Dear all:

When I run the 'generate_sensor_dataset_visualizations.py' file, it alway report the error that: No such file or directory. I check the difference and found that the error path is '/.../argv2/SensorDataset/sensor/SensorDataset_val/5589de60-1727-3e3f-9423-33437fc5da4b/sensors/lidar/315967919259399000.feather' and the true path is '/.../argv2/SensorDataset/sensor/val/5589de60-1727-3e3f-9423-33437fc5da4b/sensors/lidar/315967919259399000.feather'. Is any parameter in the program that needs to be debugged or something else? Hoping for your reply and thanks so much.

Hi folks,

I am trying to run my model in Argoverse 2.0, which was previously trained using 1.1 and its corresponding API. Nevertheless, after installing and cloning the API, in order to check the tutorials, dataloaders etc, this api looks quite smaller than Argoverse 1.1. and the organization also seems to be different (e.g. where are the csvs with the trajectories?). Where could I see all the required documentation?

hi, since there is no information about how the lane markings are labeled in the argvoerse-v2 dataset. I wonder if these lane marking labels are annotated in the originally collected point cloud (labeling in 3D space), or if it is annotated on the image by projecting the point cloud onto the corresponding image.

hope you can help me figure out and thanks in advance :)

Hey, the argoverse 2 dataset comes with new and richer scenes. Comparing the scenes of av1 to av2 in the respective cities: How similar what you consider them? So, in short: Would you say training with argoverse 2 includes all the relevant data to perform well on argoverse 1? I would be particularly interested in the motion forecasting dataset. Looking forward to your answer! Thanks a lot!

Hey everyone,

I had a look into the motion forecasting dataset and there seems to be an issue with the trajectories of the focal agent. According to the paper, the focal agent should always be observed over the full 11 seconds, which then corresponds to 110 observations: "Within each scenario, we mark a single track as the “focal agent". Focal tracks are guaranteed to be fully observed throughout the duration of the scenario and have been specifically selected to maximize interesting interactions with map features and other nearby actors (see Section 3.3.2)"

However, this is not the case for some scenarios (~3% of the scenarios). One example: Scenario '0215552f-6951-47e5-8cf6-3d1351d28957' of the validation set has a trajectory with only 104 observations.

Can you reproduce my problem? Is this intended or can we expect this to be fixed in the near future?

Looking forward hearing from you!

Best regards

SchDevel

Thanks for your excellent work! I would like to know how to do the evaluation of 3D object detection on validation split. And I notice there is PR about this. When will the stable version be released? I am looking forward to it!

Hi, thank you for providing the outstanding dataset.

I am particularly interested in the motion dataset, and have a question that is it possible to extract the route of the self-driving vehicles in each scenario?

Hi, when i run python tutorials/generate_sensor_dataset_visualizations.py -d /xxx/av2, I got the error: FileNotFoundError: [Errno 2] Failed to open local file '/xxx/av2/test/0c6e62d7-bdfa-3061-8d3d-03b13aa21f68/annotations.feather'. Detail: [errno 2] No such file or directory. The test set has no label. Why is it not filtered out in the code? What is the correct command to run this py file? Thanks.

Hi,

Sorry for the delay. Thank you for your help! I went through the dataset API and was able to isolate individual point clouds.

Joint(L), Top (R)

Top(L), Bottom (R)

Does this look sensible? Here is the code snippet. ` dataset = SensorDataloader(Path(settings.argoverse_dataset_root), with_annotations=True, with_cache=True) for index, data_frame in enumerate(dataset): sweep = data_frame.sweep # has lidar info annotations = data_frame.annotations # has boxes pose = data_frame.timestamp_city_SE3_ego_dict

    # get the lidar - both combined into single pcl
    pcl_joint = sweep.xyz

    # append reflectances and laser numbers
    pcl_joint = np.hstack([pcl_joint, np.expand_dims(sweep.intensity, -1), 
                                        np.expand_dims(sweep.laser_number, -1)])

    # laser number [0, 31] -> top lidar, [32, 63] -> bottom lidar
    r_up = np.where(pcl_joint[:, -1] < 32)
    pcl_up = pcl_joint[r_up]  # get top lidar point cloud

    r_down = np.where(pcl_joint[:, -1] >= 32)
    pcl_down = pcl_joint[r_down]

`

Please let me know if this is the correct way, just to be sure.

Best Regards Sambit

I noticed that we have two methods to get the centerline of lane_segment. First, we just get the data from raw map file. Second, we can use the function of class ArgoverseStaticMap which is "get_lane_segment_centerline" to get the centerline. I wanna know the difference of these two methods.

Hey,

you were roughly explaining the interestingness score in your paper and in the supplementaries. Are you planning to share more details about the process of selecting interesting scenarios or is this confidential functionality?

I am looking forward to your answer.

Best regards

The vector_data_json_path variable seems to extract the wrong path definition (with relative path being passed in the Map_Tutorial notebook)

Setting it to just vector_data_fname seems to be working for me -- instead of log_map_dirpath / vector_data_fname

Check it out please?

Thanks!

PR Summary

Testing

In order to ensure this PR works as intended, it is:

• [ ] unit tested.
• [ ] other or not applicable (additional detail/rationale required)

Compliance with Standards

As the author, I certify that this PR conforms to the following standards:

• [ ] Code changes conform to PEP8 and docstrings conform to the Google Python style guide.
• [ ] A well-written summary explains what was done and why it was done.
• [ ] The PR is adequately tested and the testing details and links to external results are included.

I tried to convert timestamps_ns assuming epoch format and all scenarios seem to refer to date and time in the year 1980. Has there been any deliberate anonymization of the timestamp or am I doing the conversion wrong?

Thanks in advance!