StreamMOTP: 3D多物体追跡と軌道予測を統合するストリーミングフレームワーク
StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction
自動運転における3D多物体追跡と軌道予測を、メモリバンクと相対時空間位置エンコーディング、デュアルストリーム予測器を用いてストリーミング形式で統合するフレームワークを提案し、nuScenesで有効性を示した。
著者: Jiaheng Zhuang, Guoan Wang, Siyu Zhang, Xiyang Wang, Hangning Zhou, Ziyao Xu, Chi Zhang, Zhiheng Li
分類: cs.CV, cs.RO
原文アブストラクト
3D multi-object tracking and trajectory prediction are two crucial modules in autonomous driving systems. Generally, the two tasks are handled separately in traditional paradigms and a few methods have started to explore modeling these two tasks in a joint manner recently. However, these approaches suffer from the limitations of single-frame training and inconsistent coordinate representations between tracking and prediction tasks. In this paper, we propose a streaming and unified framework for joint 3D Multi-Object Tracking and trajectory Prediction (StreamMOTP) to address the above challenges. Firstly, we construct the model in a streaming manner and exploit a memory bank to preserve and leverage the long-term latent features for tracked objects more effectively. Secondly, a relative spatio-temporal positional encoding strategy is introduced to bridge the gap of coordinate representations between the two tasks and maintain the pose-invariance for trajectory prediction. Thirdly, we further improve the quality and consistency of predicted trajectories with a dual-stream predictor. We conduct extensive experiments on popular nuSences dataset and the experimental results demonstrate the effectiveness and superiority of StreamMOTP, which outperforms previous methods significantly on both tasks. Furthermore, we also prove that the proposed framework has great potential and advantages in actual applications of autonomous driving.
