Evaluating Pedestrian Trajectory Prediction Methods for Autonomous Driving Applications
Pedestrian trajectory prediction remains a critical challenge for autonomous driving systems. This study evaluates state-of-the-art methods on their accuracy, feature requirements, and computational efficiency when generating single trajectories for practical deployment.
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System
G-PECNet improves upon the state-of-the-art PECNet model for pedestrian trajectory prediction through architectural improvements and synthetic data augmentation, achieving a 9.5% reduction in Final Displacement Error on the Stanford Drone Dataset benchmark.
Guided Full Trajectory Diffuser: A Robust and Adaptable Framework for Joint Pedestrian Trajectory Prediction
The Guided Full Trajectory Diffuser (GFTD) is a novel diffusion model-based framework that captures the joint distribution of full pedestrian trajectories, enabling robust prediction and controllable generation under various conditions, including noisy and incomplete historical data.
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