相対姿勢エンコーディングを用いた異種ポリラインTransformerによるリアルタイム動作予測
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
自動運転の動作予測をリアルタイム化するため、相対姿勢を扱う新注意機構KNARPEと階層型Transformer HPTRを提案し、エージェント中心手法と同等精度をシーン中心手法並みの効率で実現した。
著者: Zhejun Zhang, Alexander Liniger, Christos Sakaridis, Fisher Yu, Luc Van Gool
分類: cs.CV, cs.RO
原文アブストラクト
The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding traffic participants. Existing agent-centric methods have demonstrated outstanding performance on public benchmarks. However, they suffer from high computational overhead and poor scalability as the number of agents to be predicted increases. To address this problem, we introduce the K-nearest neighbor attention with relative pose encoding (KNARPE), a novel attention mechanism allowing the pairwise-relative representation to be used by Transformers. Then, based on KNARPE we present the Heterogeneous Polyline Transformer with Relative pose encoding (HPTR), a hierarchical framework enabling asynchronous token update during the online inference. By sharing contexts among agents and reusing the unchanged contexts, our approach is as efficient as scene-centric methods, while performing on par with state-of-the-art agent-centric methods. Experiments on Waymo and Argoverse-2 datasets show that HPTR achieves superior performance among end-to-end methods that do not apply expensive post-processing or model ensembling. The code is available at https://github.com/zhejz/HPTR.
