SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving
This work addresses safety improvements in autonomous driving by providing more accurate trajectory predictions, though it is incremental as it builds on an existing method.
The paper tackles pedestrian trajectory prediction for autonomous driving by enhancing the SGNet architecture with skeleton information and temporal data augmentation, achieving state-of-the-art results on JAAD and PIE datasets.
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+.