複数EKF軌道候補のニューラル融合による車両軌道予測
Vehicle Trajectory Prediction via Neural Fusion of Multiple EKF-Based Trajectory Candidates
ニューラル軌道予測器Trajectron++の出力と拡張カルマンフィルタ(EKF)による複数の軌道候補を後段で融合し、nuScenesデータセットで予測誤差を改善した。
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著者: Seong-Jun Kim, Seung-Hyun Kong
分類: cs.RO
原文アブストラクト
Predicting the future trajectories of surrounding vehicles in autonomous driving is important for collision risk assessment and safe ego-vehicle path planning. Conventional neural network-based trajectory predictors typically achieve strong prediction performance by exploiting agent history, dynamic scene graphs, and semantic maps. However, in specific motion regimes such as acceleration, deceleration, and turning, these predictors may fail to reflect physically feasible trajectories. To address this issue, this study proposes a framework that fuses the output of Trajectron++, a neural network-based trajectory predictor, with extended Kalman filter (EKF)-based multiple trajectory candidates at a late stage. On the nuScenes dataset, the proposed method reduces the average displacement error and final displacement error of the Trajectron++ robot baseline by 13.7% and 14.6%, respectively, without modifying the baseline architecture. These results indicate that EKF-based trajectory candidates can effectively complement neural trajectory prediction through learned fusion.