Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

TL;DR

This paper introduces a collaborative uncertainty concept and a CU-aware regression framework to improve multi-agent multi-modal trajectory forecasting by estimating uncertainty and selecting optimal predictions.

Contribution

It proposes a novel collaborative uncertainty measure and a permutation-equivariant estimator, enhancing existing forecasting systems with uncertainty estimation and better prediction ranking.

Findings

The framework accurately approximates the ground-truth Laplace distribution on synthetic data.

It improves SOTA systems' performance, e.g., VectorNet by 262 cm on nuScenes.

Prediction uncertainty correlates with future stochasticity and agent interactions.

Abstract

In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes correlations among the predicted trajectories of multiple agents; 2) how to rank the multiple predictions and select the optimal predicted trajectory. In order to handle these challenges, this work first proposes a novel concept, collaborative uncertainty (CU), which models the uncertainty resulting from interaction modules. Then we build a general CU-aware regression framework with an original permutation-equivariant uncertainty estimator to do both tasks of regression and uncertainty estimation. Further, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty. We conduct extensive experiments on a synthetic dataset and two public large-scale multi-agent trajectory forecasting benchmarks. Experiments show that: 1) on the synthetic dataset, the CU-aware regression framework allows the model to appropriately approximate the ground-truth Laplace distribution; 2) on the multi-agent trajectory forecasting benchmarks, the CU-aware regression framework steadily helps SOTA systems improve their performances. Specially, the proposed framework helps VectorNet improve by 262 cm regarding the Final Displacement Error of the chosen optimal prediction on the nuScenes dataset; 3) for multi-agent multi-modal trajectory forecasting systems, prediction uncertainty is positively correlated with future stochasticity; and 4) the estimated CU values are highly related to the interactive information among agents.