Technical Report for Argoverse Challenges on 4D Occupancy Forecasting
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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Datasets
Argoverse 2
Argoverse 2 Sensor
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Methods
Absolute Position Encodings •
Adam •
BPE •
Convolution •
Dense Connections •
Detr •
Dropout •
Feedforward Network •
Label Smoothing •
Layer Normalization •
Linear Layer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer
