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Motion Forecasting via Model-Based Risk Minimization

Aron Distelzweig, Eitan Kosman, Andreas Look, Faris Janjoš, Denesh K. Manivannan, Abhinav Valada

TL;DR

This paper proposes a novel sampling method applicable to trajectory prediction based on the predictions of multiple models, and introduces a new method that generates optimal trajectories from a set of neural networks, framing it as a risk minimization problem with a variable loss function.

Abstract

Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has improved prediction accuracy in various fields, its application in trajectory prediction is limited due to the multi-modal nature of predictions. In this paper, we propose a novel sampling method applicable to trajectory prediction based on the predictions of multiple models. We first show that conventional sampling based on predicted probabilities can degrade performance due to missing alignment between models. To address this problem, we introduce a new method that generates optimal trajectories from a set of neural networks, framing it as a risk minimization problem with a variable loss function. By using state-of-the-art models as base learners, our approach constructs diverse and effective ensembles for optimal trajectory sampling. Extensive experiments on the nuScenes prediction dataset demonstrate that our method surpasses current state-of-the-art techniques, achieving top ranks on the leaderboard. We also provide a comprehensive empirical study on ensembling strategies, offering insights into their effectiveness. Our findings highlight the potential of advanced ensembling techniques in trajectory prediction, significantly improving predictive performance and paving the way for more reliable predicted trajectories.

Motion Forecasting via Model-Based Risk Minimization

TL;DR

This paper proposes a novel sampling method applicable to trajectory prediction based on the predictions of multiple models, and introduces a new method that generates optimal trajectories from a set of neural networks, framing it as a risk minimization problem with a variable loss function.

Abstract

Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has improved prediction accuracy in various fields, its application in trajectory prediction is limited due to the multi-modal nature of predictions. In this paper, we propose a novel sampling method applicable to trajectory prediction based on the predictions of multiple models. We first show that conventional sampling based on predicted probabilities can degrade performance due to missing alignment between models. To address this problem, we introduce a new method that generates optimal trajectories from a set of neural networks, framing it as a risk minimization problem with a variable loss function. By using state-of-the-art models as base learners, our approach constructs diverse and effective ensembles for optimal trajectory sampling. Extensive experiments on the nuScenes prediction dataset demonstrate that our method surpasses current state-of-the-art techniques, achieving top ranks on the leaderboard. We also provide a comprehensive empirical study on ensembling strategies, offering insights into their effectiveness. Our findings highlight the potential of advanced ensembling techniques in trajectory prediction, significantly improving predictive performance and paving the way for more reliable predicted trajectories.
Paper Structure (14 sections, 3 equations, 2 figures, 2 tables, 1 algorithm)

This paper contains 14 sections, 3 equations, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Weighted Trajectory Sets
  4. Sampling Trajectories from Distributions
  5. Ensembles in Motion Prediction
  6. Technical Approach
  7. Ensembles of Trajectory Prediction Models
  8. What are Optimal Trajectories?
  9. Generating Optimal Trajectories
  10. Experimental Evaluations
  11. More Trajectory Proposals = Lower Prediction Error?
  12. Benchmarking Against Alternative Sampling Methods
  13. Influence of the Ensemble Composition
  14. Conclusion

Figures (2)

  • Figure 1: Illustration of future trajectories. Left: All 30 proposals from our ensemble, including LaPred kim2021_lapred, LAformer liu2024_laformer, and PGP deo2022_pgp, alongside the ground-truth trajectory. Middle: Five out of 30 trajectories with the highest predicted probabilities. Right: Five out of 30 trajectories generated with our proposed method.
  • Figure 2: Change in the prediction error as a function of the number of trajectory proposals w.r.t. PGP base performance. Trajectory proposals are equally generated from LaPred kim2021_lapred, LAformer liu2024_laformer, PGP deo2022_pgp. E.g.: 90 proposals consist of 30 proposals from each of the three models. Left: Using Topk sampling strategy, which is selecting the $k$ most likely trajectories. Right: Our proposed sampling strategy.