自動運転に向けた歩行者軌道予測手法の評価
Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving
自動運転での利用を想定し、ETH/UCYデータセット上で歩行者軌道予測手法を単一軌道生成の観点から評価し、観測履歴の影響や推論時間、定性比較を通じて課題を分析した。
著者: Nico Uhlemann, Felix Fent, Markus Lienkamp
分類: cs.LG, cs.RO
原文アブストラクト
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.
