# Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation

arXiv: https://arxiv.org/abs/2010.01114 · HTML: https://mlsanity.com/paper/2010.01114/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 30 of 57)

- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.05689/) (2025)
- [Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos](https://mlsanity.com/paper/2204.01696/) (2022)
- [Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning](https://mlsanity.com/paper/2201.13425/) (2022)
- [MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2108.09274/) (2021)
- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2307.09306/) (2023)
- [SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model](https://mlsanity.com/paper/2403.18452/) (2024)
- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction](https://mlsanity.com/paper/2204.11561/) (2022)
- [Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?](https://mlsanity.com/paper/2210.07681/) (2022)
- [Forecasting from LiDAR via Future Object Detection](https://mlsanity.com/paper/2203.16297/) (2022)
- [Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction](https://mlsanity.com/paper/2308.08824/) (2023)
- [SMEMO: Social Memory for Trajectory Forecasting](https://mlsanity.com/paper/2203.12446/) (2022)
- [Self-Paced Context Evaluation for Contextual Reinforcement Learning](https://mlsanity.com/paper/2106.05110/) (2021)
- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [Learning to Reason at the Frontier of Learnability](https://mlsanity.com/paper/2502.12272/) (2025)
- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [Proximal Curriculum for Reinforcement Learning Agents](https://mlsanity.com/paper/2304.12877/) (2023)
- [Intention-aware Denoising Diffusion Model for Trajectory Prediction](https://mlsanity.com/paper/2403.09190/) (2024)
- [D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights](https://mlsanity.com/paper/2207.10398/) (2022)
- [Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding](https://mlsanity.com/paper/2202.01478/) (2022)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)
- [ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2302.07583/) (2023)
- [Conditional Unscented Autoencoders for Trajectory Prediction](https://mlsanity.com/paper/2310.19944/) (2023)
- [Proximal Curriculum with Task Correlations for Deep Reinforcement Learning](https://mlsanity.com/paper/2405.02481/) (2024)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [Exploring Attention GAN for Vehicle Motion Prediction](https://mlsanity.com/paper/2209.12674/) (2022)
- [Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds](https://mlsanity.com/paper/2405.16439/) (2024)
- [Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting](https://mlsanity.com/paper/2111.13324/) (2021)

## Abstract (arXiv)

In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction. Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by a (ii) routing module which estimates a set of plausible trajectories that route towards the estimated goal. We leverage information about the past trajectory and visual context of the scene to estimate a multi-modal probability distribution over the possible goal positions, which is used to sample a potential goal during the inference. The routing is governed by a recurrent neural network that reacts to physical constraints in the nearby surroundings and generates feasible paths that route towards the sampled goal. Our extensive experimental evaluation shows that our method establishes a new state-of-the-art on several benchmarks while being able to generate a realistic and diverse set of trajectories that conform to physical constraints.