[2510.00405] EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations

Computer Science > Computer Vision and Pattern Recognition arXiv:2510.00405 (cs) [Submitted on 1 Oct 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations Authors:Jiayi Liu, Jiaming Zhou, Ke Ye, Kun-Yu Lin, Allan Wang, Junwei Liang View a PDF of the paper titled EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations, by Jiayi Liu and 5 other authors View PDF HTML (experimental) Abstract:Reliable trajectory prediction from an ego-centric perspective is crucial for robotic navigation in human-centric environments. However, existing methods typically assume noiseless observation histories, failing to account for the perceptual artifacts inherent in first-person vision, such as occlusions, ID switches, and tracking drift. This discrepancy between training assumptions and deployment reality severely limits model robustness. To bridge this gap, we introduce EgoTraj-Bench, built upon TBD dataset, which is the first real-world benchmark that aligns noisy, first-person visual histories with clean, bird's-eye-view future trajectories, enabling robust learning under realistic perceptual constraints. Building on this benchmark, we propose BiFlow, a dual-stream flow matching model that concurrently denoises historical observations and forecasts future motion. To better model agent intent, BiFlow incorporates our EgoAnchor mechanism, which conditions the prediction...