LOKI: 長期・重要意図を考慮した軌道予測データセットとモデル
LOKI: Long Term and Key Intentions for Trajectory Prediction
自動運転環境で歩行者と車両の軌道と意図を同時予測する大規模データセットLOKIを構築し、意図推論が軌道予測精度を最大27%向上させるモデルを提案した。
著者: Harshayu Girase, Haiming Gang, Srikanth Malla, Jiachen Li, Akira Kanehara, Karttikeya Mangalam, Chiho Choi
分類: cs.CV, cs.AI, cs.LG, cs.MA, cs.RO
原文アブストラクト
Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. However, the current research activities are not directly applicable to intelligent and safety critical systems. This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view. To this end, we propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction for heterogeneous traffic agents (pedestrians and vehicles) in an autonomous driving setting. The LOKI dataset is created to discover several factors that may affect intention, including i) agent's own will, ii) social interactions, iii) environmental constraints, and iv) contextual information. We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction. We show our method outperforms state-of-the-art trajectory prediction methods by upto $27\%$ and also provide a baseline for frame-wise intention estimation.
