Geometry-Guided Behavior Pattern Adaptation for Trajectory Prediction in Unseen Scenes

Yaqun Cui, Meixiu Long, Jinbiao Chen, Jianpeng Zhou, Siyuan Chen, Jiahai Wang

Abstract: Pedestrian trajectory prediction aims to forecast future trajectories based on observed behaviors and surrounding conditions, and it is critical for applications like autonomous driving. Predicting trajectories in unseen scenes is challenging due to varying environments, elusive internal movement patterns, and complex social interactions. Existing methods face two limitations. Firstly, they struggle to effectively extract internal movement patterns from historical trajectories without labeled samples, which are often inaccessible in practice. Secondly, they fail to learn social interaction patterns across scenes, particularly when using angle-related features that are noise-sensitive and not strictly invariant to Euclidean transformations. To address these challenges, this paper introduces a Geometry-guided Behavior Pattern Adaptation (GBPA) method based on two geometric observations. Firstly, properly normalized historical trajectories are distributionally similar to full trajectories, allowing generation of pseudo-full trajectories for auxiliary training. Secondly, the discretized angular partitions, created by splitting the perceptive field into equal-sized fans, are invariant to Euclidean transformations and robust to noise. GBPA employs a test-time training strategy on scaled historical trajectories (T3SH) to adapt internal movement patterns without future trajectories and an angular partitioned attention (APA) mechanism to capture transferable social interaction patterns by differentiating neighbors’ effects. Experimental results on two datasets demonstrate that GBPA significantly improves prediction performance.

External IDs:dblp:conf/ijcnn/CuiLCZCW25