Social-WaGDAT: ワッサースタイングラフ二重注意ネットワークによる相互作用を考慮した軌道予測
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
動的グラフ表現と関係性の帰納バイアスを用いてマルチエージェントの相互作用を明示的にモデル化し、将来軌道の分布を生成する軌道予測手法を提案。車両には運動学的制約層を適用し、3つの公開ベンチマークで予測精度の向上を示した。
著者: Jiachen Li, Hengbo Ma, Zhihao Zhang, Masayoshi Tomizuka
分類: cs.CV, cs.LG, cs.MA, cs.RO
原文アブストラクト
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios. Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent. In this paper, we propose a generic generative neural system (called Social-WaGDAT) for multi-agent trajectory prediction, which makes a step forward to explicit interaction modeling by incorporating relational inductive biases with a dynamic graph representation and leverages both trajectory and scene context information. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction which not only ensures physical feasibility but also enhances model performance. The proposed system is evaluated on three public benchmark datasets for trajectory prediction, where the agents cover pedestrians, cyclists and on-road vehicles. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction accuracy.
