# Goal-driven Long-Term Trajectory Prediction

arXiv: https://arxiv.org/abs/2011.02751 · HTML: https://mlsanity.com/paper/2011.02751/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 24 of 24)

- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries](https://mlsanity.com/paper/2208.01582/) (2022)
- [AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?](https://mlsanity.com/paper/2307.16368/) (2023)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums](https://mlsanity.com/paper/2110.07288/) (2021)
- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [MSN: Multi-Style Network for Trajectory Prediction](https://mlsanity.com/paper/2107.00932/) (2021)
- [DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution](https://mlsanity.com/paper/2106.14160/) (2021)
- [Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums](https://mlsanity.com/paper/2304.05106/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising](https://mlsanity.com/paper/2404.02227/) (2024)
- [HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling](https://mlsanity.com/paper/2110.02344/) (2021)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)
- [Goal-based Neural Physics Vehicle Trajectory Prediction Model](https://mlsanity.com/paper/2409.15182/) (2024)
- [Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach](https://mlsanity.com/paper/2403.06086/) (2024)
- [Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations](https://mlsanity.com/paper/2412.02447/) (2024)
- [LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application](https://mlsanity.com/paper/2406.13787/) (2024)
- [GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2402.19002/) (2024)
- [Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting](https://mlsanity.com/paper/2509.15219/) (2025)
- [VISTA: A Vision and Intent-Aware Social Attention Framework for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2511.10203/) (2025)
- [Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information](https://mlsanity.com/paper/2409.15224/) (2024)
- [MGTraj: Multi-Granularity Goal-Guided Human Trajectory Prediction with Recursive Refinement Network](https://mlsanity.com/paper/2509.09200/) (2025)
- [Scene-aware Prediction of Diverse Human Movement Goals](https://mlsanity.com/paper/2606.29942/) (2026)

## Abstract (arXiv)

The prediction of humans' short-term trajectories has advanced significantly with the use of powerful sequential modeling and rich environment feature extraction. However, long-term prediction is still a major challenge for the current methods as the errors could accumulate along the way. Indeed, consistent and stable prediction far to the end of a trajectory inherently requires deeper analysis into the overall structure of that trajectory, which is related to the pedestrian's intention on the destination of the journey. In this work, we propose to model a hypothetical process that determines pedestrians' goals and the impact of such process on long-term future trajectories. We design Goal-driven Trajectory Prediction model - a dual-channel neural network that realizes such intuition. The two channels of the network take their dedicated roles and collaborate to generate future trajectories. Different than conventional goal-conditioned, planning-based methods, the model architecture is designed to generalize the patterns and work across different scenes with arbitrary geometrical and semantic structures. The model is shown to outperform the state-of-the-art in various settings, especially in large prediction horizons. This result is another evidence for the effectiveness of adaptive structured representation of visual and geometrical features in human behavior analysis.