自動運転車のための運動予測：サーベイ
Motion Forecasting for Autonomous Vehicles: A Survey
自動運転車の意思決定に不可欠な交通参加者の将来行動予測について、問題定式化・課題・データセット・評価指標を整理し、教師あり学習と自己教師あり学習の研究を分類して展望を論じたサーベイ論文。
著者: Jianxin Shi, Jinhao Chen, Yuandong Wang, Li Sun, Chunyang Liu, Wei Xiong, Tianyu Wo
分類: cs.RO, cs.AI
原文アブストラクト
In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motion forecasting for AVs. We propose a formal problem formulation for motion forecasting and summarize the main challenges confronting this area of research. We also detail representative datasets and evaluation metrics pertinent to this field. Furthermore, this study classifies recent research into two main categories: supervised learning and self-supervised learning, reflecting the evolving paradigms in both scenario-based and perception-based motion forecasting. In the context of supervised learning, we thoroughly examine and analyze each key element of the methodology. For self-supervised learning, we summarize commonly adopted techniques. The paper concludes and discusses potential research directions, aiming to propel progress in this vital area of AV technology.
