Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Question about LaPred in Tables I and II #4

Open
caiocj1 opened this issue Nov 23, 2024 · 3 comments
Open

Question about LaPred in Tables I and II #4

caiocj1 opened this issue Nov 23, 2024 · 3 comments

Comments

@caiocj1
Copy link

caiocj1 commented Nov 23, 2024

Hi, congrats on the awesome work!

Just a question, what would be the difference in the evaluation of LaPred in Tab I (the GT det&track input, 0.588 mAP_f line) and in Table II (the GT map, 0.760 mAP_f line)?

I had understood that both of these lines were simply trained on curated data and evaluated with GT map access, which would explain why the corresponding Agentformer line is the same in both tables. So I was wondering why LaPred isn't the same.

Thanks in advance!

@caiocj1
Copy link
Author

caiocj1 commented Nov 28, 2024

Also, I would like to just confirm how the conventional models are trained with real perception inputs:

  1. Is the regression loss for example applied only on agents that are matched to GT ones?
  2. What is used as the target future trajectory, is it the predicted agents' predicted future tracks or the GT agents' actual future positions?

@yihongXU
Copy link
Collaborator

yihongXU commented Jan 2, 2025

Hi, congrats on the awesome work!

Just a question, what would be the difference in the evaluation of LaPred in Tab I (the GT det&track input, 0.588 mAP_f line) and in Table II (the GT map, 0.760 mAP_f line)?

I had understood that both of these lines were simply trained on curated data and evaluated with GT map access, which would explain why the corresponding Agentformer line is the same in both tables. So I was wondering why LaPred isn't the same.

Thanks in advance!

The 0.760 mAP is from LaPred with 6 modes and the 0.588 one is with only 5 modes, for the reason of fair comparison with AgentFormer.

@yihongXU
Copy link
Collaborator

yihongXU commented Jan 2, 2025

Also, I would like to just confirm how the conventional models are trained with real perception inputs:

  1. Is the regression loss for example applied only on agents that are matched to GT ones?

Yes. By the way, we have tried different matching strategies in our recent work https://arxiv.org/abs/2406.08113. Please have a check.

  1. What is used as the target future trajectory, is it the predicted agents' predicted future tracks or the GT agents' actual future positions?

It's the matched GT agents' actual future positions, thus there's an one-to-one matching process to run.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants