# SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving

arXiv: https://arxiv.org/abs/2503.08016 · HTML: https://mlsanity.com/paper/2503.08016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation](https://mlsanity.com/paper/2007.14558/) (2020)
- [Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection](https://mlsanity.com/paper/1702.05552/) (2017)
- [Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2206.05712/) (2022)
- [RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features](https://mlsanity.com/paper/2008.11647/) (2020)
- [FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network](https://mlsanity.com/paper/2005.07796/) (2020)
- [Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM](https://mlsanity.com/paper/2007.00113/) (2020)
- [Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning](https://mlsanity.com/paper/1803.03577/) (2018)
- [COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving](https://mlsanity.com/paper/2412.05462/) (2024)
- [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://mlsanity.com/paper/2501.13066/) (2025)
- [Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectory](https://mlsanity.com/paper/2411.04501/) (2024)
- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2402.19002/) (2024)

## Built on by (most cited first, 2 of 2)

- [2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving](https://mlsanity.com/paper/2508.21080/) (2025)
- [B'ezier Degradation Modeling for LiDAR-based Human Motion Capture](https://mlsanity.com/paper/2605.19620/) (2026)

## Abstract (arXiv)

Predicting pedestrian trajectories is essential for autonomous driving systems, as it significantly enhances safety and supports informed decision-making. Accurate predictions enable the prevention of collisions, anticipation of crossing intent, and improved overall system efficiency. In this study, we present SGNetPose+, an enhancement of the SGNet architecture designed to integrate skeleton information or body segment angles with bounding boxes to predict pedestrian trajectories from video data to avoid hazards in autonomous driving. Skeleton information was extracted using a pose estimation model, and joint angles were computed based on the extracted joint data. We also apply temporal data augmentation by horizontally flipping video frames to increase the dataset size and improve performance. Our approach achieves state-of-the-art results on the JAAD and PIE datasets using pose data with the bounding boxes, outperforming the SGNet model. Code is available on Github: SGNetPose+.