敵対的学習による相互作用を考慮したマルチエージェント追跡と確率的行動予測
Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
複数エージェントの相互作用を全体として扱い、GANを用いて将来行動を確率的に予測・追跡する枠組みを提案し、車両行動予測で有効性を示した論文。
著者: Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite. Although many research studies have been devoted to making predictions on a single entity, it remains an open challenge to forecast future behaviors for multiple interactive agents simultaneously. In this work, we take advantage of the Generative Adversarial Network (GAN) due to its capability of distribution learning and propose a generic multi-agent probabilistic prediction and tracking framework which takes the interactions among multiple entities into account, in which all the entities are treated as a whole. However, since GAN is very hard to train, we make an empirical research and present the relationship between training performance and hyperparameter values with a numerical case study. The results imply that the proposed model can capture both the mean, variance and multi-modalities of the groundtruth distribution. Moreover, we apply the proposed approach to a real-world task of vehicle behavior prediction to demonstrate its effectiveness and accuracy. The results illustrate that the proposed model trained by adversarial learning can achieve a better prediction performance than other state-of-the-art models trained by traditional supervised learning which maximizes the data likelihood. The well-trained model can also be utilized as an implicit proposal distribution for particle filtered based Bayesian state estimation.
