# Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach

arXiv: https://arxiv.org/abs/2407.20732 · HTML: https://mlsanity.com/paper/2407.20732/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [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)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding](https://mlsanity.com/paper/2003.03212/) (2020)
- [Vehicle trajectory prediction works, but not everywhere](https://mlsanity.com/paper/2112.03909/) (2021)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [The Importance of Prior Knowledge in Precise Multimodal Prediction](https://mlsanity.com/paper/2006.02636/) (2020)
- [PRANK: motion Prediction based on RANKing](https://mlsanity.com/paper/2010.12007/) (2020)
- [KI-PMF: Knowledge Integrated Plausible Motion Forecasting](https://mlsanity.com/paper/2310.12007/) (2023)
- [RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction](https://mlsanity.com/paper/2304.05856/) (2023)
- [Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment](https://mlsanity.com/paper/2211.13683/) (2022)
- [Self Supervised Clustering of Traffic Scenes using Graph Representations](https://mlsanity.com/paper/2211.15508/) (2022)
- [Clustering-based Criticality Analysis for Testing of Automated Driving Systems](https://mlsanity.com/paper/2306.12738/) (2023)

## Built on by (most cited first, 2 of 2)

- [Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories](https://mlsanity.com/paper/2506.02571/) (2025)
- [Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank](https://mlsanity.com/paper/2605.01393/) (2026)

## Abstract (arXiv)

Representing diverse and plausible future trajectories is critical for motion forecasting in autonomous driving. However, efficiently capturing these trajectories in a compact set remains challenging. This study introduces a novel approach for generating scene-specific trajectory sets tailored to different contexts, such as intersections and straight roads, by leveraging map information and actor dynamics. A deterministic goal sampling algorithm identifies relevant map regions, while our Recursive In-Distribution Subsampling (RIDS) method enhances trajectory plausibility by condensing redundant representations. Experiments on the Argoverse 2 dataset demonstrate that our method achieves up to a 10% improvement in Driving Area Compliance (DAC) compared to baseline methods while maintaining competitive displacement errors. Our work highlights the benefits of mining such scene-aware trajectory sets and how they could capture the complex and heterogeneous nature of actor behavior in real-world driving scenarios.