Multi-agent Traffic Prediction via Denoised Endpoint Distribution

TL;DR

This paper introduces a Denoised Endpoint Distribution model that improves high-speed trajectory prediction by modeling intrinsic intent and uncertainty, utilizing diffusion and transformer models to focus on agent endpoints, reducing complexity and enhancing accuracy.

Contribution

The paper presents a novel trajectory prediction model that emphasizes agent endpoints and incorporates intrinsic intent and uncertainty, advancing high-speed scenario predictions.

Findings

Model outperforms existing methods on open datasets.

Endpoint-focused approach reduces model complexity.

Ablation studies confirm the importance of intrinsic intent and uncertainty modeling.

Abstract

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatio-temporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents' spatio-temporal features alongside their intrinsic intentions and uncertainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model's efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.