Closed ggosjw closed 1 year ago
Hi, @ggosjw, thanks for your questions. We have made some modifications to the network and training to achieve a good result on the benchmark. I have some suggestions here that you might find helpful.
Got it! Thank you very much! For the second suggestion, does it mean that I should do ego vehicle prediction instead of imitation learning-based planning?
So the final loss may look like L=a1 ego_prediction+a2 surrounding_prediction+a3*score_loss.
Thank you again :)
Yes. And please remember to set the weight a2 smaller.
Hi, thank you for open-sourcing your great work.
I have questions regarding my reproduced DIPP submission results on Waymo dataset. My test dataset results are
And my validation results are
Both are worse than your results.
I preprocesssed the data using your data_process.py, changing the history timestep as 11, and future timestep as 80. I find ignoring the ego vehicle imitation loss will slightly improve the prediciton result.
Could you give some intutive suggestions regarding how to further improve the prediction performance.
Much appreciation for any help you can offer.