The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation

TL;DR

This paper introduces a novel data-driven prior framework for multi-agent trajectory interpolation that improves robustness and efficiency by implicitly modeling agent dependencies and collision avoidance, validated through comprehensive simulations.

Contribution

It proposes a new framework combining local and global data-driven priors with an efficient optimization strategy for trajectory interpolation in multi-agent systems.

Findings

Global flow prior significantly improves interpolation accuracy.

Data-driven collision priors have less impact than expected.

The method reduces computational complexity while maintaining realistic behaviors.

Abstract

Trajectory interpolation, the process of filling-in the gaps and removing noise from observed agent trajectories, is an essential task for the motion inference in multi-agent setting. A desired trajectory interpolation method should be robust to noise, changes in environments or agent densities, while also being yielding realistic group movement behaviors. Such realistic behaviors are, however, challenging to model as they require avoidance of agent-agent or agent-environment collisions and, at the same time, seek computational efficiency. In this paper, we propose a novel framework composed of data-driven priors (local, global or combined) and an efficient optimization strategy for multi-agent trajectory interpolation. The data-driven priors implicitly encode the dependencies of movements of multiple agents and the collision-avoiding desiderata, enabling elimination of costly pairwise collision constraints and resulting in reduced computational complexity and often improved estimation. Various combinations of priors and optimization algorithms are evaluated in comprehensive simulated experiments. Our experimental results reveal important insights, including the significance of the global flow prior and the lesser-than-expected influence of data-driven collision priors.