Review of Uneven Road Surface Information Perception Methods for Suspension Preview Control

Area of Science:

• Automotive Engineering
• Robotics
• Computer Vision

Background:

• Accurate road surface detection is vital for vehicle safety and comfort.
• Traditional suspension systems face real-time control challenges due to signal delays.
• Suspension preview control offers proactive adjustment but requires efficient road perception.

Purpose of the Study:

• To systematically review road surface information detection technologies for suspension preview systems.
• To focus on the identification of potholes and speed bumps.
• To provide technical references for enhancing detection accuracy and control efficiency.

Main Methods:

• Summarizing publicly available datasets for road surface analysis.
• Reviewing mainstream detection methods: traditional dynamic, 2D image processing, 3D point cloud, machine/deep learning, and multi-sensor fusion.
• Summarizing elevation reconstruction technologies using LiDAR, stereo vision, and multi-modal fusion.

Main Results:

• Comparison of mainstream detection methods based on applicable scenarios and evaluation metrics.
• Emphasis on the importance of elevation information (pothole depth, speed bump height) for preview control.
• Identification of key research directions including robustness, real-time performance, and reduced labeling costs.

Conclusions:

• The review provides a comprehensive overview of road surface detection for suspension preview.
• It highlights the significance of elevation reconstruction technologies.
• It aims to promote the development of intelligent chassis systems through improved detection and control.