Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving

TL;DR

This paper evaluates current pedestrian trajectory prediction methods for autonomous driving, analyzing their accuracy, scalability, and behavior modeling on ETH/UCY dataset to guide future improvements.

Contribution

It provides a comprehensive evaluation of state-of-the-art methods, including ablation and scalability studies, highlighting the need for incorporating additional features beyond simple models.

Findings

Constant velocity model approximates overall pedestrian dynamics

Additional features are necessary to capture common pedestrian behaviors

Prediction accuracy varies with the amount of observed motion history

Abstract

In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories, a critical aspect aligning with the requirements in autonomous systems. The evaluation is conducted on the widely-used ETH/UCY dataset where the Average Displacement Error (ADE) and the Final Displacement Error (FDE) are reported. Alongside this, we perform an ablation study to investigate the impact of the observed motion history on prediction performance. To evaluate the scalability of each approach when confronted with varying amounts of agents, the inference time of each model is measured. Following a quantitative analysis, the resulting predictions are compared in a qualitative manner, giving insight into the strengths and weaknesses of current approaches. The results demonstrate that although a constant velocity model (CVM) provides a good approximation of the overall dynamics in the majority of cases, additional features need to be incorporated to reflect common pedestrian behavior observed. Therefore, this study presents a data-driven analysis with the intent to guide the future development of pedestrian trajectory prediction algorithms.