Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning

4 Apr 2019Jiachen LiHengbo MaMasayoshi Tomizuka

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... (read more)

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