Recent Advancements in Deep Learning Techniques for Road Condition Monitoring: A Comprehensive Review

Abstract

Road Condition Monitoring is a critical task for the management and maintenance of the pavement network infrastructure by the authorities. In recent years, the application of Artificial Intelligence (AI) techniques in this domain has experienced a significant growth, driven by the continuous advancements in AI algorithms. This paper presents a comprehensive review of the latest developments in Road Condition Monitoring approaches using AI methods, with a particular focus on Deep Learning techniques, covering works published from 2020 onwards. It highlights novel approaches that have not been thoroughly explored in previous literature reviews. The literature review categorizes studies based on the type of signal data, distinguishing between acoustic, vibrational, and vision-based approaches. For each data type, the paper examines and discuss the most recent advancements and improvements achieved through AI techniques. Additionally, it provides an overview of future directions and identifying key challenges that remain open in the field. In conclusion, relatively few studies have focused on the analysis of acoustic data, although some studies have reported promising results. Methods based on vibrational data typically integrate feature extraction in frequency and wavelet domain with Convolutional Neural Networks or Long Short-Term Memory Networks. Meanwhile, vision-based methods have experienced significant improvements, driven by the constant evolution of Deep Learning architectures. A total of 173 research articles are summarized across 10 tables.

Keywords

• Artificial intelligence
• convolutional neural networks
• deep learning
• road condition monitoring
• sensors