Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning. We present a general architecture to address this challenge which models the crucial inductive biases of motion, namely, inertia, relative motion, intents and interactions. Specifically, we propose a relational model to flexibly model interactions between agents in diverse environments. Since it is well-known that human decision making is fuzzy by nature, at the core of our model lies a novel attention mechanism which models interactions by making continuous-valued (fuzzy) decisions and learning the corresponding responses. Our architecture demonstrates significant performance gains over existing state-of-the-art predictive models in diverse domains such as human crowd trajectories, US freeway traffic, NBA sports data and physics datasets. We also present ablations and augmentations to understand the decision-making process and the source of gains in our model.

Weaknesses: The experiments have been extensive, however I have following three crucial questions to better understand the performance boost arising from the overall architecture: 1. Improvement arising from interaction module or motion module? Taking Social LSTM [1] to be an interaction-based baseline, the proposed architecture has two different components: the interaction and motion modules. Is the boost coming from the interaction module which is FQA in comparison to Social Pooling [1]? Or is it the new motion module? An ablation study showing the performance while keeping the motion module the same as the baseline will help answer this question. The authors use the term Fuzzy to describe continuous-valued decisions over their discrete-valued boolean counterparts.

The architecture is based on a soft attention mechanism over pairwise relations among entities.

Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning. We present a general architecture to address this challenge which models the crucial inductive biases of motion, namely, inertia, relative motion, intents and interactions. Specifically, we propose a relational model to flexibly model interactions between agents in diverse environments. Since it is well-known that human decision making is fuzzy by nature, at the core of our model lies a novel attention mechanism which models interactions by making continuous-valued (fuzzy) decisions and learning the corresponding responses. Our architecture demonstrates significant performance gains over existing state-of-the-art predictive models in diverse domains such as human crowd trajectories, US freeway traffic, NBA sports data and physics datasets.