Mode Collapse Happens: Evaluating Critical Interactions in Joint Trajectory Prediction Models

TL;DR

This paper introduces a new evaluation framework for joint trajectory prediction models in autonomous driving, focusing on detecting mode collapse and interaction diversity to improve safety-critical predictions.

Contribution

It proposes novel metrics for evaluating mode collapse, correctness, and coverage in multi-agent trajectory predictions, emphasizing the sequential interaction dimension.

Findings

Mode collapse occurs in tested models.

Prediction accuracy improves near interaction events.

Models often fail to predict correct interaction modes before they occur.

Abstract

Autonomous Vehicle decisions rely on multimodal prediction models that account for multiple route options and the inherent uncertainty in human behavior. However, models can suffer from mode collapse, where only the most likely mode is predicted, posing significant safety risks. While existing methods employ various strategies to generate diverse predictions, they often overlook the diversity in interaction modes among agents. Additionally, traditional metrics for evaluating prediction models are dataset-dependent and do not evaluate inter-agent interactions quantitatively. To our knowledge, none of the existing metrics explicitly evaluates mode collapse. In this paper, we propose a novel evaluation framework that assesses mode collapse in joint trajectory predictions, focusing on safety-critical interactions. We introduce metrics for mode collapse, mode correctness, and coverage, emphasizing the sequential dimension of predictions. By testing four multi-agent trajectory prediction models, we demonstrate that mode collapse indeed happens. When looking at the sequential dimension, although prediction accuracy improves closer to interaction events, there are still cases where the models are unable to predict the correct interaction mode, even just before the interaction mode becomes inevitable. We hope that our framework can help researchers gain new insights and advance the development of more consistent and accurate prediction models, thus enhancing the safety of autonomous driving systems.