Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
This work addresses the problem of accurate probabilistic predictions for interactive agents, crucial for decision-making in robotics and autonomous vehicles, representing an incremental advancement by applying GANs to a known bottleneck in multi-agent forecasting.
The paper tackles the challenge of predicting future behaviors for multiple interactive agents simultaneously, proposing a multi-agent probabilistic prediction and tracking framework using Generative Adversarial Networks (GANs) that accounts for interactions, and demonstrates improved prediction performance over state-of-the-art models in real-world vehicle behavior prediction tasks.
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.