VectorFlow: 画像とベクトルを組み合わせた交通占有・流れ予測
VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction
ラスタ化画像とベクトル軌跡の両方をエンコードして融合し、道路エージェントの占有グリッドと流れを高精度に予測するモデルを提案。Waymoコンペで3位、遮蔽領域で最高性能。
著者: Xin Huang, Xiaoyu Tian, Junru Gu, Qiao Sun, Hang Zhao
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Predicting future behaviors of road agents is a key task in autonomous driving. While existing models have demonstrated great success in predicting marginal agent future behaviors, it remains a challenge to efficiently predict consistent joint behaviors of multiple agents. Recently, the occupancy flow fields representation was proposed to represent joint future states of road agents through a combination of occupancy grid and flow, which supports efficient and consistent joint predictions. In this work, we propose a novel occupancy flow fields predictor to produce accurate occupancy and flow predictions, by combining the power of an image encoder that learns features from a rasterized traffic image and a vector encoder that captures information of continuous agent trajectories and map states. The two encoded features are fused by multiple attention modules before generating final predictions. Our simple but effective model ranks 3rd place on the Waymo Open Dataset Occupancy and Flow Prediction Challenge, and achieves the best performance in the occluded occupancy and flow prediction task.
