Technical Report for Argoverse Challenges on 4D Occupancy Forecasting
Pengfei Zheng Kanokphan Lertniphonphan Feng Chen Siwei Chen Bingchuan Sun Jun Xie Zhepeng Wang
This report presents our Le3DE2E_Occ solution for 4D Occupancy Forecasting in
Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD). Our
solution consists of a strong LiDAR-based Bird's Eye View (BEV) encoder with
temporal fusion and a two-stage decoder, which combines a DETR head and a UNet
decoder. The solution was tested on the Argoverse 2 sensor dataset to evaluate
the occupancy state 3 seconds in the future. Our solution achieved 18% lower L1
Error (3.57) than the baseline and got the 1 place on the 4D Occupancy
Forecasting task in Argoverse Challenges at CVPR 2023.