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arXiv · 2311.15660

Technical Report for Argoverse Challenges on 4D Occupancy Forecasting

Pengfei Zheng·Kanokphan Lertniphonphan·Feng Chen·Siwei Chen·Bingchuan Sun·Jun Xie·Zhepeng Wang
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Abstract

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.

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Pengfei Zheng, Kanokphan Lertniphonphan, Feng Chen, Siwei Chen, Bingchuan Sun, Jun Xie, Zhepeng Wang. 2023-11-27. Technical Report for Argoverse Challenges on 4D Occupancy Forecasting. https://arxiv.org/abs/2311.15660

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