ラウンドアバウトにおける操縦ベースのアンカー軌道仮説
Maneuver-based Anchor Trajectory Hypotheses at Roundabouts
ラウンドアバウトでの車両挙動予測のため、運転意図を操縦概念で符号化し、操縦別アンカー軌道とリカレントエンコーダ・デコーダを組み合わせて多峰的な将来軌道分布を予測する手法を提案した。
著者: Mohamed Hasan, Evangelos Paschalidis, Albert Solernou, He Wang, Gustav Markkula, Richard Romano
分類: cs.CV, cs.RO
原文アブストラクト
Predicting future behavior of the surrounding vehicles is crucial for self-driving platforms to safely navigate through other traffic. This is critical when making decisions like crossing an unsignalized intersection. We address the problem of vehicle motion prediction in a challenging roundabout environment by learning from human driver data. We extend existing recurrent encoder-decoder models to be advantageously combined with anchor trajectories to predict vehicle behaviors on a roundabout. Drivers' intentions are encoded by a set of maneuvers that correspond to semantic driving concepts. Accordingly, our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout. The proposed model can output a multi-modal distribution over the predicted future trajectories based on the maneuver-specific anchors. We evaluate our model using the public RounD dataset and the experiment results show the effectiveness of the proposed maneuver-based anchor regression in improving prediction accuracy, reducing the average RMSE to 28% less than the best baseline. Our code is available at https://github.com/m-hasan-n/roundabout.
