# Multi-modal Motion Prediction using Temporal Ensembling with Learning-based Aggregation

arXiv: https://arxiv.org/abs/2410.19606 · HTML: https://mlsanity.com/paper/2410.19606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles](https://mlsanity.com/paper/1606.07839/) (2016)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving](https://mlsanity.com/paper/2109.06446/) (2021)
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://mlsanity.com/paper/2104.00249/) (2021)
- [GANet: Goal Area Network for Motion Forecasting](https://mlsanity.com/paper/2209.09723/) (2022)
- [ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals](https://mlsanity.com/paper/2303.12071/) (2023)

## Built on by (most cited first, 1 of 1)

- [LHPF: Look back the History and Plan for the Future in Autonomous Driving](https://mlsanity.com/paper/2411.17253/) (2024)

## Abstract (arXiv)

Recent years have seen a shift towards learning-based methods for trajectory prediction, with challenges remaining in addressing uncertainty and capturing multi-modal distributions. This paper introduces Temporal Ensembling with Learning-based Aggregation, a meta-algorithm designed to mitigate the issue of missing behaviors in trajectory prediction, which leads to inconsistent predictions across consecutive frames. Unlike conventional model ensembling, temporal ensembling leverages predictions from nearby frames to enhance spatial coverage and prediction diversity. By confirming predictions from multiple frames, temporal ensembling compensates for occasional errors in individual frame predictions. Furthermore, trajectory-level aggregation, often utilized in model ensembling, is insufficient for temporal ensembling due to a lack of consideration of traffic context and its tendency to assign candidate trajectories with incorrect driving behaviors to final predictions. We further emphasize the necessity of learning-based aggregation by utilizing mode queries within a DETR-like architecture for our temporal ensembling, leveraging the characteristics of predictions from nearby frames. Our method, validated on the Argoverse 2 dataset, shows notable improvements: a 4% reduction in minADE, a 5% decrease in minFDE, and a 1.16% reduction in the miss rate compared to the strongest baseline, QCNet, highlighting its efficacy and potential in autonomous driving.