Related papers: Technical Report for Argoverse Challenges on 4D Occupancy Forecasting

Technical Report for Argoverse Challenges on 4D Occupancy Forecasting

URL: http://arxiv.org/abs/2311.15660v1
Date: Mon, 27 Nov 2023 09:40:53 GMT
Title: Technical Report for Argoverse Challenges on 4D Occupancy Forecasting
Authors: Pengfei Zheng, Kanokphan Lertniphonphan, Feng Chen, Siwei Chen, Bingchuan Sun, Jun Xie, Zhepeng Wang
Abstract summary: Our solution consists of a strong LiDAR-based Bird's Eye View (BEV) encoder with temporal fusion and a two-stage 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.
Score: 32.43324720856606
License: http://creativecommons.org/licenses/by/4.0/
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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