A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions

TL;DR

This paper provides a comprehensive review of 3D object detection methods in autonomous driving, analyzing traditional techniques, recent advancements, and future research directions, including cooperative perception and end-to-end learning frameworks.

Contribution

It offers a broad survey of existing 3D detection approaches, compares their strengths and limitations, and discusses future directions and an active repository for ongoing updates.

Findings

Traditional methods vary in accuracy and robustness

Advancements include temporal perception and cooperative methods

Future directions focus on end-to-end learning and improved accuracy

Abstract

In recent years, 3D object perception has become a crucial component in the development of autonomous driving systems, providing essential environmental awareness. However, as perception tasks in autonomous driving evolve, their variants have increased, leading to diverse insights from industry and academia. Currently, there is a lack of comprehensive surveys that collect and summarize these perception tasks and their developments from a broader perspective. This review extensively summarizes traditional 3D object detection methods, focusing on camera-based, LiDAR-based, and fusion detection techniques. We provide a comprehensive analysis of the strengths and limitations of each approach, highlighting advancements in accuracy and robustness. Furthermore, we discuss future directions, including methods to improve accuracy such as temporal perception, occupancy grids, and end-to-end learning frameworks. We also explore cooperative perception methods that extend the perception range through collaborative communication. By providing a holistic view of the current state and future developments in 3D object perception, we aim to offer a more comprehensive understanding of perception tasks for autonomous driving. Additionally, we have established an active repository to provide continuous updates on the latest advancements in this field, accessible at: https://github.com/Fishsoup0/Autonomous-Driving-Perception.