知識認識グラフトランスフォーマーによる歩行者軌道予測
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction
データセット間の環境・状況の違いを考慮し、自己注意機構とドメイン適応を組み合わせたグラフトランスフォーマーで歩行者軌道予測の汎化性能を向上させた研究。
著者: Yu Liu, Yuexin Zhang, Kunming Li, Yongliang Qiao, Stewart Worrall, You-Fu Li, He Kong
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.
