A Joint Prediction Method of Multi-Agent to Reduce Collision Rate

TL;DR

This paper introduces a joint prediction method for multiple agents in autonomous driving, focusing on generating scene-consistent trajectories to reduce collision rates, building upon the SIMPL baseline and tested on Argoverse 2.

Contribution

The paper proposes a novel joint prediction approach that improves scene consistency and collision avoidance over existing marginal prediction models.

Findings

Significantly reduces collision rates compared to the SIMPL baseline.

Successfully generates scene-consistent trajectories on the Argoverse 2 dataset.

Enhances multi-agent motion prediction accuracy in autonomous driving scenarios.

Abstract

Predicting future motions of road participants is an important task for driving autonomously. Most existing models excel at predicting the marginal trajectory of a single agent, but predicting joint trajectories for multiple agents that are consistent within a scene remains a challenge. Previous research has often focused on marginal predictions, but the importance of joint predictions has become increasingly apparent. Joint prediction aims to generate trajectories that are consistent across the entire scene. Our research builds upon the SIMPL baseline to explore methods for generating scene-consistent trajectories. We tested our algorithm on the Argoverse 2 dataset, and experimental results demonstrate that our approach can generate scene-consistent trajectories. Compared to the SIMPL baseline, our method significantly reduces the collision rate of joint trajectories within the scene.