# Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

arXiv: https://arxiv.org/abs/1910.08041 · HTML: https://mlsanity.com/paper/1910.08041/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 30 of 34)

- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [HOME: Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2105.10968/) (2021)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021)
- [Occupancy Flow Fields for Motion Forecasting in Autonomous Driving](https://mlsanity.com/paper/2203.03875/) (2022)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation](https://mlsanity.com/paper/2503.09950/) (2025)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [Collaborative Uncertainty in Multi-Agent Trajectory Forecasting](https://mlsanity.com/paper/2110.13947/) (2021)
- [Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving](https://mlsanity.com/paper/2109.01510/) (2021)
- [StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving](https://mlsanity.com/paper/2206.00991/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2207.05195/) (2022)
- [OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment](https://mlsanity.com/paper/2404.02263/) (2024)
- [Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting](https://mlsanity.com/paper/2101.02385/) (2021)
- [Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories](https://mlsanity.com/paper/2106.02598/) (2021)
- [A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering](https://mlsanity.com/paper/2108.12820/) (2021)
- [Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems](https://mlsanity.com/paper/2305.01773/) (2023)
- [CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction](https://mlsanity.com/paper/2109.02965/) (2021)
- [FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow](https://mlsanity.com/paper/2305.01622/) (2023)
- [EgoCogNav: Cognition-aware Human Egocentric Navigation](https://mlsanity.com/paper/2511.17581/) (2025)
- [Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes](https://mlsanity.com/paper/2104.03956/) (2021)
- [On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models](https://mlsanity.com/paper/2206.00608/) (2022)
- [ProspectNet: Weighted Conditional Attention for Future Interaction Modeling in Behavior Prediction](https://mlsanity.com/paper/2208.13848/) (2022)
- [EgoTraj: Real-World Egocentric Human Trajectory Dataset for Multimodal Prediction](https://mlsanity.com/paper/2605.19004/) (2026)
- [GAD-Generative Learning for HD Map-Free Autonomous Driving](https://mlsanity.com/paper/2405.00515/) (2024)

## Abstract (arXiv)

Self-driving vehicles plan around both static and dynamic objects, applying predictive models of behavior to estimate future locations of the objects in the environment. However, future behavior is inherently uncertain, and models of motion that produce deterministic outputs are limited to short timescales. Particularly difficult is the prediction of human behavior. In this work, we propose the discrete residual flow network (DRF-Net), a convolutional neural network for human motion prediction that captures the uncertainty inherent in long-range motion forecasting. In particular, our learned network effectively captures multimodal posteriors over future human motion by predicting and updating a discretized distribution over spatial locations. We compare our model against several strong competitors and show that our model outperforms all baselines.