Core Insights - The article discusses the development of a novel framework called Diffusion², designed specifically for momentary trajectory prediction in autonomous driving scenarios, addressing the challenge of pedestrian trajectory prediction when limited observational data is available [1][52]. Background and Contributions - Accurate pedestrian trajectory prediction is crucial for enhancing vehicle safety, especially in human-vehicle interaction scenarios. Traditional methods often rely on longer observation periods, which may not be feasible in real-world situations where pedestrians suddenly appear from blind spots [2][52]. - The study highlights the frequency of momentary observations in datasets, with rates of 2.22 s⁻¹ in the SDD dataset and 1.02 s⁻¹ in the ETH/UCY dataset, emphasizing the need for models that can predict trajectories with limited data [2]. - The proposed Diffusion² model consists of two sequential diffusion models: one for backward prediction of unobserved historical trajectories and another for forward prediction of future trajectories, capturing the causal dependencies between these components [6][7]. Model Architecture - Diffusion² employs a dual diffusion model architecture, incorporating a dual-headed parameterization mechanism to quantify the aleatoric uncertainty of the predicted historical trajectories. This mechanism enhances the model's ability to handle noise in the predictions [4][5][7]. - A time-adaptive noise scheduling module is introduced, which dynamically adjusts the noise scale during the forward diffusion process based on the estimated uncertainty, allowing for more robust trajectory predictions [5][22]. Experimental Results - The Diffusion² model achieved state-of-the-art (SOTA) performance in momentary trajectory prediction tasks across multiple datasets, including ETH/UCY and Stanford Drone datasets, outperforming existing methods [7][44]. - The results indicate significant improvements in average displacement error (ADE) and final displacement error (FDE) metrics compared to previous models, showcasing the effectiveness of the proposed approach [44]. Limitations and Future Work - Despite its successes, Diffusion² faces inherent limitations, particularly in interactive and dense scenarios, where its adaptability may decrease. Future work aims to enhance the model's efficiency and robustness in more complex traffic environments [52][54]. - The article suggests exploring more efficient training and inference methods to reduce computational costs while maintaining prediction quality [53].