Autonomous Driving in Unstructured Environments: How Far Have We Come?

TL;DR

This survey reviews over 250 papers on autonomous driving in unstructured outdoor environments, highlighting current progress, challenges, and future research directions in areas like perception, mapping, and path planning.

Contribution

It provides a comprehensive overview of existing research, datasets, and emerging trends in autonomous driving in unstructured outdoor environments, fostering further advancements.

Findings

Significant progress in perception and mapping techniques.

Identification of key challenges like scene complexity and environmental diversity.

Emerging trends include deep learning applications and robust sensor fusion.

Abstract

Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-pose unique obstacles that are not common in structured urban areas. Despite these difficulties, autonomous driving in unstructured outdoor environments is crucial for applications in agriculture, mining, and military operations. Our survey reviews over 250 papers for autonomous driving in unstructured outdoor environments, covering offline mapping, pose estimation, environmental perception, path planning, end-to-end autonomous driving, datasets, and relevant challenges. We also discuss emerging trends and future research directions. This review aims to consolidate knowledge and encourage further research for autonomous driving in unstructured environments. To support ongoing work, we maintain an active repository with up-to-date literature and open-source projects at: https://github.com/chaytonmin/Survey-Autonomous-Driving-in-Unstructured-Environments.