# Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

arXiv: https://arxiv.org/abs/2204.11561 · HTML: https://mlsanity.com/paper/2204.11561/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [Automatic Goal Generation for Reinforcement Learning Agents](https://mlsanity.com/paper/1705.06366/) (2017)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020)
- [SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction](https://mlsanity.com/paper/1903.02793/) (2019)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction](https://mlsanity.com/paper/1912.06445/) (2019)
- [MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction](https://mlsanity.com/paper/2006.03340/) (2020)
- [MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2108.09274/) (2021)
- [Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data](https://mlsanity.com/paper/2001.03093/) (2020)
- [Social NCE: Contrastive Learning of Socially-aware Motion Representations](https://mlsanity.com/paper/2012.11717/) (2020)
- [Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation](https://mlsanity.com/paper/2010.01114/) (2020)
- [Learning Actionable Representations with Goal-Conditioned Policies](https://mlsanity.com/paper/1811.07819/) (2018)
- [CAR-Net: Clairvoyant Attentive Recurrent Network](https://mlsanity.com/paper/1711.10061/) (2017)
- [Looking to Relations for Future Trajectory Forecast](https://mlsanity.com/paper/1905.08855/) (2019)

## Built on by (most cited first, 29 of 29)

- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Adapting to Length Shift: FlexiLength Network for Trajectory Prediction](https://mlsanity.com/paper/2404.00742/) (2024)
- [Joint Metrics Matter: A Better Standard for Trajectory Forecasting](https://mlsanity.com/paper/2305.06292/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Intention-aware Denoising Diffusion Model for Trajectory Prediction](https://mlsanity.com/paper/2403.09190/) (2024)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)
- [Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking](https://mlsanity.com/paper/2411.00174/) (2024)
- [ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2302.07583/) (2023)
- [Distilling Knowledge for Short-to-Long Term Trajectory Prediction](https://mlsanity.com/paper/2305.08553/) (2023)
- [Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning](https://mlsanity.com/paper/2303.13986/) (2023)
- [GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.14922/) (2023)
- [CCF: Cross Correcting Framework for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2406.00749/) (2024)
- [Remember and Recall: Associative-Memory-based Trajectory Prediction](https://mlsanity.com/paper/2410.02201/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2508.07146/) (2025)
- [Learning a Pedestrian Social Behavior Dictionary](https://mlsanity.com/paper/2212.01426/) (2022)
- [Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints](https://mlsanity.com/paper/2309.17338/) (2023)
- [GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2312.04479/) (2023)
- [PMM-Net: Single-stage Multi-agent Trajectory Prediction with Patching-based Embedding and Explicit Modal Modulation](https://mlsanity.com/paper/2410.19544/) (2024)
- [VISTA: A Vision and Intent-Aware Social Attention Framework for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2511.10203/) (2025)
- [KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art](https://mlsanity.com/paper/2210.04366/) (2022)
- [Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines](https://mlsanity.com/paper/2402.03457/) (2024)
- [A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2406.18050/) (2024)
- [Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics](https://mlsanity.com/paper/2503.16639/) (2025)
- [MGTraj: Multi-Granularity Goal-Guided Human Trajectory Prediction with Recursive Refinement Network](https://mlsanity.com/paper/2509.09200/) (2025)
- [Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos](https://mlsanity.com/paper/2606.18824/) (2026)
- [TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2607.11939/) (2026)
- [Probabilistic Pedestrian Forecasts from a Handheld Phone: World-Frame Heat Maps, Visual-Inertial Height Drift, and Evaluation without Ground Truth](https://mlsanity.com/paper/2610.04736/) (2026)

## Abstract (arXiv)

Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multi-modality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions using complex fusion mechanisms. In this respect, we propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. Although this backbone already provides promising results, we demonstrate that its prediction accuracy can be improved considerably when combined with a scene-aware goal-estimation module. To this end, we employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations. We conduct extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY) and show that our approach performs on par with state-of-the-art techniques while reducing model complexity.