arXiv:2106.14160
공식 코드 · Papers with Code
DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution
원문 초록
In autonomous driving, goal-based multi-trajectory prediction methods are proved to be effective recently, where they first score goal candidates, then select a final set of goals, and finally complete trajectories based on the selected goals. However, these methods usually involve goal predictions based on sparse predefined anchors. In this work, we propose an anchor-free model, named DenseTNT, which performs dense goal probability estimation for trajectory prediction. Our model achieves state-of-the-art performance, and ranks 1st on the Waymo Open Dataset Motion Prediction Challenge. Project page is at https://github.com/Tsinghua-MARS-Lab/DenseTNT.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
TrajFlow: Multi-modal Motion Prediction via Flow Matching
GRIP: Graph-based Interaction-aware Trajectory Prediction
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction
Where, What, Why: Towards Explainable Driver Attention Prediction
Out-of-Distribution Semantic Occupancy Prediction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.