レーンアテンション：車両のレーンへの注意を学習する軌道予測
Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes
道路上のレーンを非ユークリッド構造として扱い、車両の走行履歴を時空間グラフに展開してグラフニューラルネットワークとアテンション機構で周囲車両の将来軌道を予測する手法を提案した。
著者: Jiacheng Pan, Hongyi Sun, Kecheng Xu, Yifei Jiang, Xiangquan Xiao, Jiangtao Hu, Jinghao Miao
分類: cs.LG, cs.CV, cs.RO, stat.ML
原文アブストラクト
Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work treats the on-road lanes as non-Euclidean structures, unfolds the vehicle's moving history to form a spatio-temporal graph, and uses methods from Graph Neural Networks to solve the problem. Not only is our approach a pioneering attempt in using non-Euclidean methods to process static environmental features around a predicted object, our model also outperforms other state-of-the-art models in several metrics. The practicability and interpretability analysis of the model shows great potential for large-scale deployment in various autonomous driving systems in addition to our own.
