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Papers/TENET: Transformer Encoding Network for Effective Temporal...

TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction

Yuting Wang, Hangning Zhou, Zhigang Zhang, Chen Feng, Huadong Lin, Chaofei Gao, Yizhi Tang, Zhenting Zhao, Shiyu Zhang, Jie Guo, Xuefeng Wang, Ziyao Xu, Chi Zhang

2022-06-30motion predictionMotion ForecastingAutonomous DrivingPredictionTrajectory Prediction
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Abstract

This technical report presents an effective method for motion prediction in autonomous driving. We develop a Transformer-based method for input encoding and trajectory prediction. Besides, we propose the Temporal Flow Header to enhance the trajectory encoding. In the end, an efficient K-means ensemble method is used. Using our Transformer network and ensemble method, we win the first place of Argoverse 2 Motion Forecasting Challenge with the state-of-the-art brier-minFDE score of 1.90.

Results

TaskDatasetMetricValueModel
Autonomous VehiclesArgoverse CVPR 2020DAC (K=6)0.9863MacFormer
Autonomous VehiclesArgoverse CVPR 2020MR (K=1)0.5596MacFormer
Autonomous VehiclesArgoverse CVPR 2020MR (K=6)0.1272MacFormer
Autonomous VehiclesArgoverse CVPR 2020brier-minFDE (K=6)1.7667MacFormer
Autonomous VehiclesArgoverse CVPR 2020minADE (K=1)1.6565MacFormer
Autonomous VehiclesArgoverse CVPR 2020minADE (K=6)0.8121MacFormer
Autonomous VehiclesArgoverse CVPR 2020minFDE (K=1)3.6081MacFormer
Autonomous VehiclesArgoverse CVPR 2020minFDE (K=6)1.2141MacFormer
Motion ForecastingArgoverse CVPR 2020DAC (K=6)0.9863MacFormer
Motion ForecastingArgoverse CVPR 2020MR (K=1)0.5596MacFormer
Motion ForecastingArgoverse CVPR 2020MR (K=6)0.1272MacFormer
Motion ForecastingArgoverse CVPR 2020brier-minFDE (K=6)1.7667MacFormer
Motion ForecastingArgoverse CVPR 2020minADE (K=1)1.6565MacFormer
Motion ForecastingArgoverse CVPR 2020minADE (K=6)0.8121MacFormer
Motion ForecastingArgoverse CVPR 2020minFDE (K=1)3.6081MacFormer
Motion ForecastingArgoverse CVPR 2020minFDE (K=6)1.2141MacFormer
Autonomous DrivingArgoverse CVPR 2020DAC (K=6)0.9863MacFormer
Autonomous DrivingArgoverse CVPR 2020MR (K=1)0.5596MacFormer
Autonomous DrivingArgoverse CVPR 2020MR (K=6)0.1272MacFormer
Autonomous DrivingArgoverse CVPR 2020brier-minFDE (K=6)1.7667MacFormer
Autonomous DrivingArgoverse CVPR 2020minADE (K=1)1.6565MacFormer
Autonomous DrivingArgoverse CVPR 2020minADE (K=6)0.8121MacFormer
Autonomous DrivingArgoverse CVPR 2020minFDE (K=1)3.6081MacFormer
Autonomous DrivingArgoverse CVPR 2020minFDE (K=6)1.2141MacFormer

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