# PRANK: motion Prediction based on RANKing

arXiv: https://arxiv.org/abs/2010.12007 · HTML: https://mlsanity.com/paper/2010.12007/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 22 of 22)

- [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)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction](https://mlsanity.com/paper/2104.00563/) (2021)
- [HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding](https://mlsanity.com/paper/2205.09753/) (2022)
- [PlanT: Explainable Planning Transformers via Object-Level Representations](https://mlsanity.com/paper/2210.14222/) (2022)
- [Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks](https://mlsanity.com/paper/2107.07455/) (2021)
- [ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation](https://mlsanity.com/paper/2307.14187/) (2023)
- [Vehicle trajectory prediction works, but not everywhere](https://mlsanity.com/paper/2112.03909/) (2021)
- [StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving](https://mlsanity.com/paper/2206.00991/) (2022)
- [OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment](https://mlsanity.com/paper/2404.02263/) (2024)
- [MoST: Multi-modality Scene Tokenization for Motion Prediction](https://mlsanity.com/paper/2404.19531/) (2024)
- [FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding](https://mlsanity.com/paper/2406.14422/) (2024)
- [Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.29031/) (2026)
- [Trajectory Forecasting on Temporal Graphs](https://mlsanity.com/paper/2207.00255/) (2022)
- [Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories](https://mlsanity.com/paper/2506.02571/) (2025)
- [Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach](https://mlsanity.com/paper/2407.20732/) (2024)
- [Kraken: enabling joint trajectory prediction by utilizing Mode Transformer and Greedy Mode Processing](https://mlsanity.com/paper/2312.05144/) (2023)
- [BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposals](https://mlsanity.com/paper/2509.10080/) (2025)
- [Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models](https://mlsanity.com/paper/2510.17274/) (2025)
- [Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank](https://mlsanity.com/paper/2605.01393/) (2026)

## Abstract (arXiv)

Predicting the motion of agents such as pedestrians or human-driven vehicles is one of the most critical problems in the autonomous driving domain. The overall safety of driving and the comfort of a passenger directly depend on its successful solution. The motion prediction problem also remains one of the most challenging problems in autonomous driving engineering, mainly due to high variance of the possible agent's future behavior given a situation. The two phenomena responsible for the said variance are the multimodality caused by the uncertainty of the agent's intent (e.g., turn right or move forward) and uncertainty in the realization of a given intent (e.g., which lane to turn into). To be useful within a real-time autonomous driving pipeline, a motion prediction system must provide efficient ways to describe and quantify this uncertainty, such as computing posterior modes and their probabilities or estimating density at the point corresponding to a given trajectory. It also should not put substantial density on physically impossible trajectories, as they can confuse the system processing the predictions. In this paper, we introduce the PRANK method, which satisfies these requirements. PRANK takes rasterized bird-eye images of agent's surroundings as an input and extracts features of the scene with a convolutional neural network. It then produces the conditional distribution of agent's trajectories plausible in the given scene. The key contribution of PRANK is a way to represent that distribution using nearest-neighbor methods in latent trajectory space, which allows for efficient inference in real time. We evaluate PRANK on the in-house and Argoverse datasets, where it shows competitive results.