一般高速道路シナリオにおける軌道予測のための運動学モデル
A Kinematic Model for Trajectory Prediction in General Highway Scenarios
車間追従と車線変更の運動学モデルを提案し、様々な高速道路シナリオでの軌道予測に拡張した。多様なセンシング条件下の実験で最先端手法を上回る性能を示した。
著者: Cyrus Anderson, Ram Vasudevan, Matthew Johnson-Roberson
分類: cs.RO
原文アブストラクト
Highway driving invariably combines high speeds with the need to interact closely with other drivers. Prediction methods enable autonomous vehicles (AVs) to anticipate drivers' future trajectories and plan accordingly. Kinematic methods for prediction have traditionally ignored the presence of other drivers, or made predictions only for a limited set of scenarios. Data-driven approaches fill this gap by learning from large datasets to predict trajectories in general scenarios. While they achieve high accuracy, they also lose the interpretability and tools for model validation enjoyed by kinematic methods. This letter proposes a novel kinematic model to describe car-following and lane change behavior, and extends it to predict trajectories in general scenarios. Experiments on highway datasets under varied sensing conditions demonstrate that the proposed method outperforms state-of-the-art methods.
