Motion Forecasting for Autonomous Vehicles: A Survey

TL;DR

This survey comprehensively reviews recent advances in motion forecasting for autonomous vehicles, covering problem formulation, challenges, datasets, evaluation metrics, and learning paradigms, to guide future research.

Contribution

It provides a formal problem framework, classifies recent methods into supervised and self-supervised learning, and discusses key challenges and future directions in AV motion forecasting.

Findings

Classified research into supervised and self-supervised learning approaches.

Summarized key datasets and evaluation metrics used in the field.

Identified main challenges and proposed future research directions.

Abstract

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.