Deep-learning based non-contact method for assessing pavement skid resistance using 3D laser imaging technology

ABSTRACT

Traditional contact and water-based testing devices exhibit some limitations in evaluating pavement skid resistance. This study demonstrates the viability of deep learning (DL) based non-contact pavement skid resistance evaluation using 3D laser imaging technology. Pavement 3D images and friction numbers were collected from 28 field sites in 10 states in the U.S. to cover pavement sections with various texture and friction characteristics. A total number of 28,000 pairs of 3D images and friction numbers were prepared as the database to train and validate the friction prediction model, named as FrictionNet-V, using the convolutional neural network. The input of FrictionNet-V is the 3D pavement image from the left wheel path, and the output is the corresponding friction number ranging from 0.2 to 0.9. Based on the experimental results on 5,600 testing images, FrictionNet-V achieved better performance than other DL models in terms of speed and accuracy. The result demonstrates the potential of highway speed non-contact pavement skid resistance evaluation using high resolution 3D texture images and DL methods at the network level in the future.

KEYWORDS:

• Pavement safety
• pavement friction
• 3D image
• deep learning
• convolutional neural network
• pavement skid resistance
• pavement management system

Acknowledgments

This paper was prepared under the research project, ‘Long Term Performance Monitoring of High Friction Surfacing Treatments (HFST) Sites’, sponsored by the Federal Highway Administration (FHWA). The opinions expressed in the paper are those of the authors, who are responsible for the accuracy of the facts and data herein and do not necessarily reflect the official policies of the sponsoring agency. This paper does not constitute a standard, regulation, or specification.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by Federal Highway Administration: [Grant Number Long Term Performance Monitoring of HFST Sites].