# QML for Argoverse 2 Motion Forecasting Challenge

arXiv: https://arxiv.org/abs/2207.06553 · HTML: https://mlsanity.com/paper/2207.06553/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network](https://mlsanity.com/paper/2103.16273/) (2021)

## Built on by (most cited first, 8 of 8)

- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders](https://mlsanity.com/paper/2308.09882/) (2023)
- [Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM](https://mlsanity.com/paper/2503.10898/) (2025)
- [Motion Forecasting in Continuous Driving](https://mlsanity.com/paper/2410.06007/) (2024)
- [HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning](https://mlsanity.com/paper/2505.15703/) (2025)
- [LANet: A Lane Boundaries-Aware Approach For Robust Trajectory Prediction](https://mlsanity.com/paper/2507.01308/) (2025)
- [Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting](https://mlsanity.com/paper/2406.14415/) (2024)
- [Context-aware Attention-based Gaussian Mixture Models for Vehicular Trajectory Prediction](https://mlsanity.com/paper/2610.09174/) (2026)

## Abstract (arXiv)

To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.