综述自动驾驶行为预测方法与挑战，助力智能驾驶决策。
Motion Forecasting for Autonomous Vehicles: A Survey
- 提出行为预测问题形式化定义，梳理核心挑战。
- 分类总结监督与自监督学习方法，覆盖主流技术路径。
- 整理关键数据集与评估指标，适合研究者参考。
近年来，自动驾驶领域受到广泛关注。准确预测各类交通参与者未来行为对自动驾驶车辆（AV）的决策至关重要。本文聚焦于基于场景和基于感知的运动预测，提出运动预测的形式化问题定义，并总结该领域的关键挑战。同时，详细介绍了代表性数据集与评估指标。研究将近年工作分为监督学习与自监督学习两大类，系统分析了监督学习中各关键环节，归纳了自监督学习常用技术。最后，讨论潜在研究方向，旨在推动自动驾驶核心技术的发展。
原文摘要 · Abstract (English)
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.
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