Using a Diffusion Model for Pedestrian Trajectory Prediction in Semi-Open Autonomous Driving Environments

Abstract

In recent years, the pervasive deployment and progression of autonomous driving technology have engendered heightened demands, particularly within the intricate campus and surrounding environments frequently traversed by autonomous delivery vehicles, such as automated food delivery and courier services. Accurately predicting pedestrian trajectories is paramount in the realm of autonomous driving. In the face of complex scenarios within campus and surrounding environments, traditional pedestrian trajectory prediction methods have failed to achieve satisfactory results. To address this challenge systematically, this paper employs a digital twin methodology to establish a novel dataset, denoted as the vulnerable pedestrian trajectory prediction dataset (VPT), grounded in the authentic road network structures of six campuses and their environs. This paper proposed a UTD-PTP trajectory prediction framework based on the diffusion model, which seeks to forecast pedestrian trajectories in settings characterized by heightened pedestrian traffic, disorderliness, and irregularities. Importantly, the applicability of our proposed methodology extends beyond campus environments, showcasing commendable performance on standard autonomous driving datasets. Experimental results reveal an average enhancement of 0.03 in ADE and 0.05 in FDE on publicly available datasets. On the VPT dataset, our method demonstrates substantial improvements of 0.12 in ADE and 0.38 in FDE relative to the baseline model. Overall, our proposed method exhibits superiority in pedestrian trajectory prediction models, substantially reinforcing confidence in the safety of vulnerable road users in autonomous driving.