Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Area of Science:

• Robotics
• Computer Vision
• Transportation Engineering

Background:

• Light Detection and Ranging (LiDAR) offers high accuracy and wide perception, unaffected by lighting conditions.
• Roadside LiDAR deployment provides a top-down view of traffic scenes, enabling real-time object trajectory tracking.
• Integrating roadside LiDAR with autonomous vehicles significantly boosts local perception capabilities.

Purpose of the Study:

• To systematically review roadside LiDAR perception methods for autonomous driving.
• To analyze object detection techniques using single and cooperative roadside LiDAR systems.
• To discuss challenges and future research directions in this domain.

Main Methods:

• Review of current object detection algorithms for single roadside LiDAR.
• Analysis of cooperative multi-LiDAR systems for enhanced traffic scene perception.
• Examination of studies addressing adverse weather conditions for LiDAR perception.
• Introduction to recent datasets for roadside LiDAR research.

Main Results:

• Identification of key challenges in roadside LiDAR object detection.
• Overview of existing methods for both single and cooperative LiDAR setups.
• Highlighting the importance of advanced wireless communication for data distribution.
• Presentation of relevant datasets and adverse weather studies.

Conclusions:

• Roadside LiDAR is crucial for improving autonomous vehicle perception.
• Systematic review provides a comprehensive understanding of current methods and datasets.
• Future research should focus on addressing open challenges for practical applications.
• This work serves as a guide for advancing roadside LiDAR perception technology.