# Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss

arXiv: https://arxiv.org/abs/2211.00149 · HTML: https://mlsanity.com/paper/2211.00149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [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)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction](https://mlsanity.com/paper/2104.00563/) (2021)
- [HOME: Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2105.10968/) (2021)
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://mlsanity.com/paper/2110.06607/) (2021)
- [Reciprocal Learning Networks for Human Trajectory Prediction](https://mlsanity.com/paper/2004.04340/) (2020)
- [Bootstrap Motion Forecasting With Self-Consistent Constraints](https://mlsanity.com/paper/2204.05859/) (2022)
- [Trajectory Prediction with Graph-based Dual-scale Context Fusion](https://mlsanity.com/paper/2111.01592/) (2021)
- [TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction](https://mlsanity.com/paper/2207.00170/) (2022)

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

- [Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors](https://mlsanity.com/paper/2608.00675/) (2026)

## Abstract (arXiv)

Robust motion forecasting of the dynamic scene is a critical component of an autonomous vehicle. It is a challenging problem due to the heterogeneity in the scene and the inherent uncertainties in the problem. To improve the accuracy of motion forecasting, in this work, we identify a new consistency constraint in this task, that is an agent's future trajectory should be coherent with its history observations and visa versa. To leverage this property, we propose a novel cycle consistency training scheme and define a novel cycle loss to encourage this consistency. In particular, we reverse the predicted future trajectory backward in time and feed it back into the prediction model to predict the history and compute the loss as an additional cycle loss term. Through our experiments on the Argoverse dataset, we demonstrate that cycle loss can improve the performance of competitive motion forecasting models.